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<title>Jat Ai News &amp; Unveiling the Future of Intelligence &amp; : AWS</title>
<link>https://news.jatlink.uk/rss/category/tools-aws</link>
<description>Jat Ai News &amp; Unveiling the Future of Intelligence &amp; : AWS</description>
<dc:language>en</dc:language>
<dc:rights>Copyright 2024 Jat Link Limited &amp; All Rights Reserved.</dc:rights>

<item>
<title>Building Workforce AI Agents with Visier and Amazon Quick</title>
<link>https://news.jatlink.uk/9698</link>
<guid>https://news.jatlink.uk/9698</guid>
<description><![CDATA[ In this post, we show how connecting the Visier Workforce AI platform with Amazon Quick through Model Context Protocol (MCP) gives every knowledge worker a unified agentic workspace to ask questions in. Visier helps ground the workspace in live workforce data and the organizational context that surrounds it while letting your users act on the conversational results without switching tools. ]]></description>
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<pubDate>Fri, 24 Apr 2026 23:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, Workforce, Agents, with, Visier, and, Amazon, Quick</media:keywords>
</item>

<item>
<title>Amazon Quick for marketing: From scattered data to strategic action</title>
<link>https://news.jatlink.uk/9596</link>
<guid>https://news.jatlink.uk/9596</guid>
<description><![CDATA[ Amazon Quick changes how you work. You can set it up in minutes and by the end of the day, you will wonder how you ever worked without it. Quick connects with your applications, tools, and data, creating a personal knowledge graph that learns your priorities, preferences, and network. ]]></description>
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<pubDate>Thu, 23 Apr 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, for, marketing:, From, scattered, data, strategic, action</media:keywords>
</item>

<item>
<title>Applying multimodal biological foundation models across therapeutics and patient care</title>
<link>https://news.jatlink.uk/9597</link>
<guid>https://news.jatlink.uk/9597</guid>
<description><![CDATA[ In this post, we&#039;ll explore how multimodal BioFMs work, showcase real-world applications in drug discovery and clinical development, and contextualize how AWS enables organizations to build and deploy multimodal BioFMs. ]]></description>
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<pubDate>Thu, 23 Apr 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Applying, multimodal, biological, foundation, models, across, therapeutics, and, patient, care</media:keywords>
</item>

<item>
<title>Cost&amp;effective multilingual audio transcription at scale with Parakeet&amp;TDT and AWS Batch</title>
<link>https://news.jatlink.uk/9530</link>
<guid>https://news.jatlink.uk/9530</guid>
<description><![CDATA[ In this post, we walk through building a scalable, event-driven transcription pipeline that automatically processes audio files uploaded to Amazon Simple Storage Service (Amazon S3), and show you how to use Amazon EC2 Spot Instances and buffered streaming inference to further reduce costs. ]]></description>
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<pubDate>Wed, 22 Apr 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Cost-effective, multilingual, audio, transcription, scale, with, Parakeet-TDT, and, AWS, Batch</media:keywords>
</item>

<item>
<title>Amazon SageMaker AI now supports optimized generative AI inference recommendations</title>
<link>https://news.jatlink.uk/9531</link>
<guid>https://news.jatlink.uk/9531</guid>
<description><![CDATA[ Today, Amazon SageMaker AI  supports optimized generative AI inference recommendations. By delivering validated, optimal deployment configurations with performance metrics, Amazon SageMaker AI keeps your model developers focused on building accurate models, not managing infrastructure. ]]></description>
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<pubDate>Wed, 22 Apr 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, now, supports, optimized, generative, inference, recommendations</media:keywords>
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<item>
<title>Get to your first working agent in minutes: Announcing new features in Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/9532</link>
<guid>https://news.jatlink.uk/9532</guid>
<description><![CDATA[ Today, we&#039;re introducing new capabilities that further streamline the agent building experience, removing the infrastructure barriers that slow teams down at every stage of agent development from the first prototype through production deployment. ]]></description>
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<pubDate>Wed, 22 Apr 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Get, your, first, working, agent, minutes:, Announcing, new, features, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Company&amp;wise memory in Amazon Bedrock with Amazon Neptune and Mem0</title>
<link>https://news.jatlink.uk/9516</link>
<guid>https://news.jatlink.uk/9516</guid>
<description><![CDATA[ Company-wise memory in Amazon Bedrock, powered by Amazon Neptune and Mem0, provides AI agents with persistent, company-specific context—enabling them to learn, adapt, and respond intelligently across multiple interactions. TrendMicro, one of the largest antivirus software companies in the world, developed the Trend’s Companion chatbot, so their customers can explore information through natural, conversational interactions ]]></description>
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<pubDate>Wed, 22 Apr 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Company-wise, memory, Amazon, Bedrock, with, Amazon, Neptune, and, Mem0</media:keywords>
</item>

<item>
<title>From developer desks to the whole organization: Running Claude Cowork in Amazon Bedrock</title>
<link>https://news.jatlink.uk/9447</link>
<guid>https://news.jatlink.uk/9447</guid>
<description><![CDATA[ Today, we&#039;re excited to announce Claude Cowork in Amazon Bedrock. You can now run Cowork and Claude Code Desktop through Amazon Bedrock, directly or using an LLM gateway. In this post, we walk through how Claude Cowork integrates with Amazon Bedrock and show an example of how knowledge workers use it in practice. ]]></description>
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<pubDate>Tue, 21 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, developer, desks, the, whole, organization:, Running, Claude, Cowork, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>End&amp;to&amp;end lineage with DVC and Amazon SageMaker AI MLflow apps</title>
<link>https://news.jatlink.uk/9433</link>
<guid>https://news.jatlink.uk/9433</guid>
<description><![CDATA[ In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage. We walk through two deployable patterns — dataset-level lineage and record-level lineage — that you can run in your own AWS account using the companion notebooks. ]]></description>
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<pubDate>Tue, 21 Apr 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>End-to-end, lineage, with, DVC, and, Amazon, SageMaker, MLflow, apps</media:keywords>
</item>

<item>
<title>Accelerate Generative AI Inference on Amazon SageMaker AI with G7e Instances</title>
<link>https://news.jatlink.uk/9362</link>
<guid>https://news.jatlink.uk/9362</guid>
<description><![CDATA[ Today, we are thrilled to announce the availability of G7e instances powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs on Amazon SageMaker AI. You can provision nodes with 1, 2, 4, and 8 RTX PRO 6000 GPU instances, with each GPU providing 96 GB of GDDR7 memory. This launch provides the capability to use a single-node GPU, G7e.2xlarge instance to host powerful open source foundation models (FMs) like GPT-OSS-120B, Nemotron-3-Super-120B-A12B (NVFP4 variant), and Qwen3.5-35B-A3B, offering organizations a cost-effective and high-performing option. ]]></description>
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<pubDate>Mon, 20 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, Generative, Inference, Amazon, SageMaker, with, G7e, Instances</media:keywords>
</item>

<item>
<title>ToolSimulator: scalable tool testing for AI agents</title>
<link>https://news.jatlink.uk/9363</link>
<guid>https://news.jatlink.uk/9363</guid>
<description><![CDATA[ You can use ToolSimulator, an LLM-powered tool simulation framework within Strands Evals, to thoroughly and safely test AI agents that rely on external tools, at scale. Instead of risking live API calls that expose personally identifiable information (PII), trigger unintended actions, or settling for static mocks that break with multi-turn workflows, you can use ToolSimulator&#039;s large language model (LLM)-powered simulations to validate your agents. Available today as part of the Strands Evals Software Development Kit (SDK), ToolSimulator helps you catch integration bugs early, test edge cases comprehensively, and ship production-ready agents with confidence. ]]></description>
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<pubDate>Mon, 20 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ToolSimulator:, scalable, tool, testing, for, agents</media:keywords>
</item>

<item>
<title>Omnichannel ordering with Amazon Bedrock AgentCore and Amazon Nova 2 Sonic</title>
<link>https://news.jatlink.uk/9348</link>
<guid>https://news.jatlink.uk/9348</guid>
<description><![CDATA[ In this post, we&#039;ll show you how to build a complete omnichannel ordering system using Amazon Bedrock AgentCore, an agentic platform, to build, deploy, and operate highly effective AI agents securely at scale using any framework and foundation model and Amazon Nova 2 Sonic. ]]></description>
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<pubDate>Mon, 20 Apr 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Omnichannel, ordering, with, Amazon, Bedrock, AgentCore, and, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>Introducing granular cost attribution for Amazon Bedrock</title>
<link>https://news.jatlink.uk/9154</link>
<guid>https://news.jatlink.uk/9154</guid>
<description><![CDATA[ In this post, we share how Amazon Bedrock&#039;s granular cost attribution works and walk through example cost tracking scenarios. ]]></description>
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<pubDate>Sat, 18 Apr 2026 02:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, granular, cost, attribution, for, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>From hours to minutes: How Agentic AI gave marketers time back for what matters</title>
<link>https://news.jatlink.uk/9139</link>
<guid>https://news.jatlink.uk/9139</guid>
<description><![CDATA[ In this post, we share how AWS Marketing’s Technology, AI, and Analytics (TAA) team worked with Gradial to build an agentic AI solution on Amazon Bedrock for accelerating content publishing workflows. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, hours, minutes:, How, Agentic, gave, marketers, time, back, for, what, matters</media:keywords>
</item>

<item>
<title>Optimize video semantic search intent with Amazon Nova Model Distillation on Amazon Bedrock</title>
<link>https://news.jatlink.uk/9136</link>
<guid>https://news.jatlink.uk/9136</guid>
<description><![CDATA[ In this post, we show you how to use Model Distillation, a model customization technique on Amazon Bedrock, to transfer routing intelligence from a large teacher model (Amazon Nova Premier) into a much smaller student model (Amazon Nova Micro). This approach cuts inference cost by over 95% and reduces latency by 50% while maintaining the nuanced routing quality that the task demands. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Optimize, video, semantic, search, intent, with, Amazon, Nova, Model, Distillation, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Power video semantic search with Amazon Nova Multimodal Embeddings</title>
<link>https://news.jatlink.uk/9137</link>
<guid>https://news.jatlink.uk/9137</guid>
<description><![CDATA[ In this post, we show you how to build a video semantic search solution on Amazon Bedrock using Nova Multimodal Embeddings that intelligently understands user intent and retrieves accurate video results across all signal types simultaneously. We also share a reference implementation you can deploy and explore with your own content. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Power, video, semantic, search, with, Amazon, Nova, Multimodal, Embeddings</media:keywords>
</item>

<item>
<title>Nova Forge SDK series part 2: Practical guide to fine&amp;tune Nova models using data mixing capabilities</title>
<link>https://news.jatlink.uk/9138</link>
<guid>https://news.jatlink.uk/9138</guid>
<description><![CDATA[ This hands-on guide walks through every step of fine-tuning an Amazon Nova model with the Amazon Nova Forge SDK, from data preparation to training with data mixing to evaluation, giving you a repeatable playbook you can adapt to your own use case. This is the second part in our Nova Forge SDK series, building on the SDK introduction and first part, which covered kicking off customization experiments. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Nova, Forge, SDK, series, part, Practical, guide, fine-tune, Nova, models, using, data, mixing, capabilities</media:keywords>
</item>

<item>
<title>Cost&amp;efficient custom text&amp;to&amp;SQL using Amazon Nova Micro and Amazon Bedrock on&amp;demand inference</title>
<link>https://news.jatlink.uk/9049</link>
<guid>https://news.jatlink.uk/9049</guid>
<description><![CDATA[ In this post, we demonstrate two approaches to fine-tune Amazon Nova Micro for custom SQL dialect generation to deliver both cost efficiency and production ready performance. ]]></description>
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<pubDate>Thu, 16 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Cost-efficient, custom, text-to-SQL, using, Amazon, Nova, Micro, and, Amazon, Bedrock, on-demand, inference</media:keywords>
</item>

<item>
<title>Transform retail with AWS generative AI services</title>
<link>https://news.jatlink.uk/9050</link>
<guid>https://news.jatlink.uk/9050</guid>
<description><![CDATA[ Online retailers face a persistent challenge: shoppers struggle to determine the fit and look when ordering online, leading to increased returns and decreased purchase confidence. The cost? Lost revenue, operational overhead, and customer frustration. Meanwhile, consumers increasingly expect immersive, interactive shopping experiences that bridge the gap between online and in-store retail. Retailers implementing virtual try-on […] ]]></description>
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<pubDate>Thu, 16 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Transform, retail, with, AWS, generative, services</media:keywords>
</item>

<item>
<title>How Automated Reasoning checks in Amazon Bedrock transform generative AI compliance</title>
<link>https://news.jatlink.uk/9051</link>
<guid>https://news.jatlink.uk/9051</guid>
<description><![CDATA[ In this post, you&#039;ll learn why probabilistic AI validation falls short in regulated industries and how Automated Reasoning checks use formal verification to deliver mathematically proven results. You&#039;ll also see how customers across six industries use this technology to produce formally verified, auditable AI outputs, and how to get started. ]]></description>
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<pubDate>Thu, 16 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Automated, Reasoning, checks, Amazon, Bedrock, transform, generative, compliance</media:keywords>
</item>

<item>
<title>Create rich, custom tooltips in Amazon Quick Sight</title>
<link>https://news.jatlink.uk/8936</link>
<guid>https://news.jatlink.uk/8936</guid>
<description><![CDATA[ Today, we&#039;re announcing sheet tooltips in Amazon Quick Sight. Dashboard authors can now design custom tooltip layouts using free-form layout sheets. These layouts combine charts, key performance indicator (KPI) metrics, text, and other visuals into a single tooltip that renders dynamically when readers hover over data points. ]]></description>
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<pubDate>Wed, 15 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Create, rich, custom, tooltips, Amazon, Quick, Sight</media:keywords>
</item>

<item>
<title>Accelerating decode&amp;heavy LLM inference with speculative decoding on AWS Trainium and vLLM</title>
<link>https://news.jatlink.uk/8937</link>
<guid>https://news.jatlink.uk/8937</guid>
<description><![CDATA[ In this post, you will learn how speculative decoding works and why it helps reduce cost per generated token on AWS Trainium2. ]]></description>
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<pubDate>Wed, 15 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, decode-heavy, LLM, inference, with, speculative, decoding, AWS, Trainium, and, vLLM</media:keywords>
</item>

<item>
<title>Rede Mater Dei de Saúde: Monitoring AI agents in the revenue cycle with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/8938</link>
<guid>https://news.jatlink.uk/8938</guid>
<description><![CDATA[ This post is cowritten by Renata Salvador Grande, Gabriel Bueno and Paulo Laurentys at Rede Mater Dei de Saúde. The growing adoption of multi-agent AI systems is redefining critical operations in healthcare. In large hospital networks, where thousands of decisions directly impact cash flow, service delivery times, and the risk of claim denials, the ability […] ]]></description>
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<pubDate>Wed, 15 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rede, Mater, Dei, Saúde:, Monitoring, agents, the, revenue, cycle, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Navigating the generative AI journey: The Path&amp;to&amp;Value framework from AWS</title>
<link>https://news.jatlink.uk/8864</link>
<guid>https://news.jatlink.uk/8864</guid>
<description><![CDATA[ In this post, we introduce the Generative AI Path-to-Value (P2V) framework, a structured approach to help you move generative AI initiatives from concept to production and sustained value creation. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Navigating, the, generative, journey:, The, Path-to-Value, framework, from, AWS</media:keywords>
</item>

<item>
<title>Use&amp;case based deployments on SageMaker JumpStart</title>
<link>https://news.jatlink.uk/8865</link>
<guid>https://news.jatlink.uk/8865</guid>
<description><![CDATA[ We&#039;re excited to announce the launch of Amazon SageMaker JumpStart optimized deployments. SageMaker JumpStart improved deployments address the need for rich and straightforward deployment customization on SageMaker JumpStart by offering pre-defined deployment configurations, designed for specific use cases. Customers maintain the same level of visibility into the details of their proposed deployments, but now deployments are optimized for their specific use case and performance constraint. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Use-case, based, deployments, SageMaker, JumpStart</media:keywords>
</item>

<item>
<title>Best practices to run inference on Amazon SageMaker HyperPod</title>
<link>https://news.jatlink.uk/8866</link>
<guid>https://news.jatlink.uk/8866</guid>
<description><![CDATA[ This post explores how Amazon SageMaker HyperPod provides a comprehensive solution for inference workloads. We walk you through the platform’s key capabilities for dynamic scaling, simplified deployment, and intelligent resource management. By the end of this post, you’ll understand how to use the HyperPod automated infrastructure, cost optimization features, and performance enhancements to reduce your total cost of ownership by up to 40% while accelerating your generative AI deployments from concept to production. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Best, practices, run, inference, Amazon, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>How Guidesly built AI&amp;generated trip reports for outdoor guides on AWS</title>
<link>https://news.jatlink.uk/8867</link>
<guid>https://news.jatlink.uk/8867</guid>
<description><![CDATA[ In this post, we walk through how Guidesly built Jack AI on AWS using AWS Lambda, AWS Step Functions, Amazon Simple Storage Service (Amazon S3), Amazon Relational Database Service (Amazon RDS), Amazon SageMaker AI, and Amazon Bedrock to ingest trip media, enrich it with context, apply computer vision and generative AI, and publish marketing-ready content across multiple channels—securely, reliably, and at scale. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Guidesly, built, AI-generated, trip, reports, for, outdoor, guides, AWS</media:keywords>
</item>

<item>
<title>Spring AI SDK for Amazon Bedrock AgentCore is now Generally Available</title>
<link>https://news.jatlink.uk/8827</link>
<guid>https://news.jatlink.uk/8827</guid>
<description><![CDATA[ With the new Spring AI AgentCore SDK, you can build production-ready AI agents and run them on the highly scalable AgentCore Runtime. The Spring AI AgentCore SDK is an open source library that brings Amazon Bedrock AgentCore capabilities into Spring AI. In this post, we build an AI agent starting with a chat endpoint, then adding streaming responses, conversation memory, and tools for web browsing and code execution. ]]></description>
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<pubDate>Tue, 14 Apr 2026 14:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Spring, SDK, for, Amazon, Bedrock, AgentCore, now, Generally, Available</media:keywords>
</item>

<item>
<title>How to build effective reward functions with AWS Lambda for Amazon Nova model customization</title>
<link>https://news.jatlink.uk/8762</link>
<guid>https://news.jatlink.uk/8762</guid>
<description><![CDATA[ This post demonstrates how Lambda enables scalable, cost-effective reward functions for Amazon Nova customization. You&#039;ll learn to choose between Reinforcement Learning via Verifiable Rewards (RLVR) for objectively verifiable tasks and Reinforcement Learning via AI Feedback (RLAIF) for subjective evaluation, design multi-dimensional reward systems that help you prevent reward hacking, optimize Lambda functions for training scale, and monitor reward distributions with Amazon CloudWatch. Working code examples and deployment guidance are included to help you start experimenting. ]]></description>
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<pubDate>Mon, 13 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, build, effective, reward, functions, with, AWS, Lambda, for, Amazon, Nova, model, customization</media:keywords>
</item>

<item>
<title>Understanding Amazon Bedrock model lifecycle</title>
<link>https://news.jatlink.uk/8471</link>
<guid>https://news.jatlink.uk/8471</guid>
<description><![CDATA[ This post shows you how to manage FM transitions in Amazon Bedrock, so you can make sure your AI applications remain operational as models evolve. We discuss the three lifecycle states, how to plan migrations with the new extended access feature, and practical strategies to transition your applications to newer models without disruption. ]]></description>
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<pubDate>Thu, 09 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Understanding, Amazon, Bedrock, model, lifecycle</media:keywords>
</item>

<item>
<title>The future of managing agents at scale: AWS Agent Registry now in preview</title>
<link>https://news.jatlink.uk/8472</link>
<guid>https://news.jatlink.uk/8472</guid>
<description><![CDATA[ Today, we&#039;re announcing AWS Agent Registry (preview) in AgentCore, a single place to discover, share, and reuse AI agents, tools, and agent skills across your enterprise. ]]></description>
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<pubDate>Thu, 09 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, future, managing, agents, scale:, AWS, Agent, Registry, now, preview</media:keywords>
</item>

<item>
<title>Embed a live AI browser agent in your React app with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/8473</link>
<guid>https://news.jatlink.uk/8473</guid>
<description><![CDATA[ This post walks you through three steps: starting a session and generating the Live View URL, rendering the stream in your React application, and wiring up an AI agent that drives the browser while your users watch. At the end, you will have a working sample application you can clone and run. ]]></description>
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<pubDate>Thu, 09 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Embed, live, browser, agent, your, React, app, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Introducing stateful MCP client capabilities on Amazon Bedrock AgentCore Runtime</title>
<link>https://news.jatlink.uk/8453</link>
<guid>https://news.jatlink.uk/8453</guid>
<description><![CDATA[ In this post, you will learn how to build stateful MCP servers that request user input during execution, invoke LLM sampling for dynamic content generation, and stream progress updates for long-running tasks. You will see code examples for each capability and deploy a working stateful MCP server to Amazon Bedrock AgentCore Runtime. ]]></description>
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<pubDate>Thu, 09 Apr 2026 18:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, stateful, MCP, client, capabilities, Amazon, Bedrock, AgentCore, Runtime</media:keywords>
</item>

<item>
<title>Human&amp;in&amp;the&amp;loop constructs for agentic workflows in healthcare and life sciences</title>
<link>https://news.jatlink.uk/8374</link>
<guid>https://news.jatlink.uk/8374</guid>
<description><![CDATA[ In healthcare and life sciences, AI agents help organizations process clinical data, submit regulatory filings, automate medical coding, and accelerate drug development and commercialization. However, the sensitive nature of healthcare data and regulatory requirements like Good Practice (GxP) compliance require human oversight at key decision points. This is where human-in-the-loop (HITL) constructs become essential. In this post, you will learn four practical approaches to implementing human-in-the-loop constructs using AWS services. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Human-in-the-loop, constructs, for, agentic, workflows, healthcare, and, life, sciences</media:keywords>
</item>

<item>
<title>Building intelligent audio search with Amazon Nova Embeddings: A deep dive into semantic audio understanding</title>
<link>https://news.jatlink.uk/8375</link>
<guid>https://news.jatlink.uk/8375</guid>
<description><![CDATA[ This post walks you through understanding audio embeddings, implementing Amazon Nova Multimodal Embeddings, and building a practical search system for your audio content. You&#039;ll learn how embeddings represent audio as vectors, explore the technical capabilities of Amazon Nova, and see hands-on code examples for indexing and querying your audio libraries. By the end, you&#039;ll have the knowledge to deploy production-ready audio search capabilities. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, intelligent, audio, search, with, Amazon, Nova, Embeddings:, deep, dive, into, semantic, audio, understanding</media:keywords>
</item>

<item>
<title>Reinforcement fine&amp;tuning on Amazon Bedrock: Best practices</title>
<link>https://news.jatlink.uk/8376</link>
<guid>https://news.jatlink.uk/8376</guid>
<description><![CDATA[ In this post, we explore where RFT is most effective, using the GSM8K mathematical reasoning dataset as a concrete example. We then walk through best practices for dataset preparation and reward function design, show how to monitor training progress using Amazon Bedrock metrics, and conclude with practical hyperparameter tuning guidelines informed by experiments across multiple models and use cases. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reinforcement, fine-tuning, Amazon, Bedrock:, Best, practices</media:keywords>
</item>

<item>
<title>Customize Amazon Nova models with Amazon Bedrock fine&amp;tuning</title>
<link>https://news.jatlink.uk/8373</link>
<guid>https://news.jatlink.uk/8373</guid>
<description><![CDATA[ In this post, we&#039;ll walk you through a complete implementation of model fine-tuning in Amazon Bedrock using Amazon Nova models, demonstrating each step through an intent classifier example that achieves superior performance on a domain specific task. Throughout this guide, you&#039;ll learn to prepare high-quality training data that drives meaningful model improvements, configure hyperparameters to optimize learning without overfitting, and deploy your fine-tuned model for improved accuracy and reduced latency. We&#039;ll show you how to evaluate your results using training metrics and loss curves. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Customize, Amazon, Nova, models, with, Amazon, Bedrock, fine-tuning</media:keywords>
</item>

<item>
<title>Manage AI costs with Amazon Bedrock Projects</title>
<link>https://news.jatlink.uk/8297</link>
<guid>https://news.jatlink.uk/8297</guid>
<description><![CDATA[ With Amazon Bedrock Projects, you can attribute inference costs to specific workloads and analyze them in AWS Cost Explorer and AWS Data Exports. In this post, you will learn how to set up Projects end-to-end, from designing a tagging strategy to analyzing costs. ]]></description>
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<pubDate>Wed, 08 Apr 2026 02:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Manage, costs, with, Amazon, Bedrock, Projects</media:keywords>
</item>

<item>
<title>Building real&amp;time conversational podcasts with Amazon Nova 2 Sonic</title>
<link>https://news.jatlink.uk/8267</link>
<guid>https://news.jatlink.uk/8267</guid>
<description><![CDATA[ This post walks through building an automated podcast generator that creates engaging conversations between two AI hosts on any topic, demonstrating the streaming capabilities of Nova Sonic, stage-aware content filtering, and real-time audio generation. ]]></description>
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<pubDate>Tue, 07 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, real-time, conversational, podcasts, with, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>Text&amp;to&amp;SQL solution powered by Amazon Bedrock</title>
<link>https://news.jatlink.uk/8268</link>
<guid>https://news.jatlink.uk/8268</guid>
<description><![CDATA[ In this post, we show you how to build a natural text-to-SQL solution using Amazon Bedrock that transforms business questions into database queries and returns actionable answers. ]]></description>
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<pubDate>Tue, 07 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Text-to-SQL, solution, powered, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>From isolated alerts to contextual intelligence: Agentic maritime anomaly analysis with generative AI</title>
<link>https://news.jatlink.uk/8202</link>
<guid>https://news.jatlink.uk/8202</guid>
<description><![CDATA[ This blog post demonstrates how Windward helps enhance and accelerate alert investigation processes by combining geospatial intelligence with generative AI, enabling analysts to focus on decision-making rather than data collection. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, isolated, alerts, contextual, intelligence:, Agentic, maritime, anomaly, analysis, with, generative</media:keywords>
</item>

<item>
<title>Build AI&amp;powered employee onboarding agents with Amazon Quick</title>
<link>https://news.jatlink.uk/8199</link>
<guid>https://news.jatlink.uk/8199</guid>
<description><![CDATA[ In this post, we walk through building a custom HR onboarding agent with Quick. We show how to configure an agent that understands your organization’s processes, connects to your HR systems, and automates common tasks, such as answering new-hire questions and tracking document completion. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-powered, employee, onboarding, agents, with, Amazon, Quick</media:keywords>
</item>

<item>
<title>Accelerate agentic tool calling with serverless model customization in Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/8200</link>
<guid>https://news.jatlink.uk/8200</guid>
<description><![CDATA[ In this post, we walk through how we fine-tuned Qwen 2.5 7B Instruct for tool calling using RLVR. We cover dataset preparation across three distinct agent behaviors, reward function design with tiered scoring, training configuration and results interpretation, evaluation on held-out data with unseen tools, and deployment. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, agentic, tool, calling, with, serverless, model, customization, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Building Intelligent Search with Amazon Bedrock and Amazon OpenSearch for hybrid RAG solutions</title>
<link>https://news.jatlink.uk/8201</link>
<guid>https://news.jatlink.uk/8201</guid>
<description><![CDATA[ In this post, we show how to implement a generative AI agentic assistant that uses both semantic and text-based search using Amazon Bedrock, Amazon Bedrock AgentCore, Strands Agents and Amazon OpenSearch. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, Intelligent, Search, with, Amazon, Bedrock, and, Amazon, OpenSearch, for, hybrid, RAG, solutions</media:keywords>
</item>

<item>
<title>Connecting MCP servers to Amazon Bedrock AgentCore Gateway using Authorization Code flow</title>
<link>https://news.jatlink.uk/8185</link>
<guid>https://news.jatlink.uk/8185</guid>
<description><![CDATA[ Amazon Bedrock AgentCore Gateway provides a centralized layer for managing how AI agents connect to tools and MCP servers across your organization. In this post, we walk through how to configure AgentCore Gateway to connect to an OAuth-protected MCP server using the Authorization Code flow. ]]></description>
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<pubDate>Mon, 06 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Connecting, MCP, servers, Amazon, Bedrock, AgentCore, Gateway, using, Authorization, Code, flow</media:keywords>
</item>

<item>
<title>Simulate realistic users to evaluate multi&amp;turn AI agents in Strands Evals</title>
<link>https://news.jatlink.uk/7929</link>
<guid>https://news.jatlink.uk/7929</guid>
<description><![CDATA[ In this post, we explore how ActorSimulator in Strands Evaluations SDK addresses the challenge with structured user simulation that integrates into your evaluation pipeline. ]]></description>
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<pubDate>Thu, 02 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Simulate, realistic, users, evaluate, multi-turn, agents, Strands, Evals</media:keywords>
</item>

<item>
<title>Control which domains your AI agents can access</title>
<link>https://news.jatlink.uk/7909</link>
<guid>https://news.jatlink.uk/7909</guid>
<description><![CDATA[ In this post, we show you how to configure AWS Network Firewall to restrict AgentCore resources to an allowlist of approved internet domains. This post focuses on domain-level filtering using SNI inspection — the first layer of a defense-in-depth approach. ]]></description>
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<pubDate>Thu, 02 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Control, which, domains, your, agents, can, access</media:keywords>
</item>

<item>
<title>Scaling seismic foundation models on AWS: Distributed training with Amazon SageMaker HyperPod and expanding context windows</title>
<link>https://news.jatlink.uk/7908</link>
<guid>https://news.jatlink.uk/7908</guid>
<description><![CDATA[ This post describes how TGS achieved near-linear scaling for distributed training and expanded context windows for their Vision Transformer-based SFM using Amazon SageMaker HyperPod. This joint solution cut training time from 6 months to just 5 days while enabling analysis of seismic volumes larger than previously possible. ]]></description>
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<pubDate>Thu, 02 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, seismic, foundation, models, AWS:, Distributed, training, with, Amazon, SageMaker, HyperPod, and, expanding, context, windows</media:keywords>
</item>

<item>
<title>Persist session state with filesystem configuration and execute shell commands</title>
<link>https://news.jatlink.uk/7890</link>
<guid>https://news.jatlink.uk/7890</guid>
<description><![CDATA[ In this post, we go through how to use managed session storage to persist your agent&#039;s filesystem state and how to execute shell commands directly in your agent&#039;s environment. ]]></description>
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<pubDate>Thu, 02 Apr 2026 14:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Persist, session, state, with, filesystem, configuration, and, execute, shell, commands</media:keywords>
</item>

<item>
<title>Rocket Close transforms mortgage document processing with Amazon Bedrock and Amazon Textract</title>
<link>https://news.jatlink.uk/7889</link>
<guid>https://news.jatlink.uk/7889</guid>
<description><![CDATA[ Through a strategic partnership with the AWS Generative AI Innovation Center (GenAIIC), Rocket Close developed an intelligent document processing solution that has significantly reduced processing time, making the process 15 times faster. The solution, which uses Amazon Textract for OCR processing and Amazon Bedrock for foundation models (FMs), achieves a strong 90% overall accuracy in document segmentation, classification, and field extraction. ]]></description>
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<pubDate>Thu, 02 Apr 2026 14:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rocket, Close, transforms, mortgage, document, processing, with, Amazon, Bedrock, and, Amazon, Textract</media:keywords>
</item>

<item>
<title>Automating competitive price intelligence with Amazon Nova Act</title>
<link>https://news.jatlink.uk/7825</link>
<guid>https://news.jatlink.uk/7825</guid>
<description><![CDATA[ This post demonstrates how to build an automated competitive price intelligence system that streamlines manual workflows, supporting teams to make data-driven pricing decisions with real-time market insights. ]]></description>
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<pubDate>Wed, 01 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automating, competitive, price, intelligence, with, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Build reliable AI agents with Amazon Bedrock AgentCore Evaluations</title>
<link>https://news.jatlink.uk/7744</link>
<guid>https://news.jatlink.uk/7744</guid>
<description><![CDATA[ In this post, we introduce Amazon Bedrock AgentCore Evaluations, a fully managed service for assessing AI agent performance across the development lifecycle. We walk through how the service measures agent accuracy across multiple quality dimensions. We explain the two evaluation approaches for development and production and share practical guidance for building agents you can deploy with confidence. ]]></description>
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<pubDate>Wed, 01 Apr 2026 02:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, reliable, agents, with, Amazon, Bedrock, AgentCore, Evaluations</media:keywords>
</item>

<item>
<title>Building an AI powered system for compliance evidence collection</title>
<link>https://news.jatlink.uk/7726</link>
<guid>https://news.jatlink.uk/7726</guid>
<description><![CDATA[ In this post, we show you how to build a similar system for your organization. You will learn the architecture decisions, implementation details, and deployment process that can help you automate your own compliance workflows. ]]></description>
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<pubDate>Tue, 31 Mar 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, powered, system, for, compliance, evidence, collection</media:keywords>
</item>

<item>
<title>Build a FinOps agent using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/7725</link>
<guid>https://news.jatlink.uk/7725</guid>
<description><![CDATA[ In this post, you learn how to build a FinOps agent using Amazon Bedrock AgentCore that helps your finance team manage AWS costs across multiple accounts. This conversational agent consolidates data from AWS Cost Explorer, AWS Budgets, and AWS Compute Optimizer into a single interface, so your team can ask questions like &quot;What are my top cost drivers this month?&quot; and receive immediate answers. ]]></description>
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<pubDate>Tue, 31 Mar 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, FinOps, agent, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Accelerating software delivery with agentic QA automation using Amazon Nova Act</title>
<link>https://news.jatlink.uk/7727</link>
<guid>https://news.jatlink.uk/7727</guid>
<description><![CDATA[ In this post, we demonstrate how to implement agentic QA automation through QA Studio, a reference solution built with Amazon Nova Act. You will see how to define tests in natural language that adapt automatically to UI changes, explore the serverless architecture that executes tests reliably at scale, and get step-by-step deployment guidance for your AWS environment. ]]></description>
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<pubDate>Tue, 31 Mar 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, software, delivery, with, agentic, automation, using, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Can your governance keep pace with your AI ambitions? AI risk intelligence in the agentic era</title>
<link>https://news.jatlink.uk/7695</link>
<guid>https://news.jatlink.uk/7695</guid>
<description><![CDATA[ Traditional frameworks designed for static deployments cannot address the dynamic interactions that define agentic workloads. AI Risk Intelligence (AIRI), from AWS Generative AI Innovation Center, provides the automated rigor required to govern agents at enterprise scale—a fundamental reimagining of how security, operations, and governance work together systemically. ]]></description>
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<pubDate>Tue, 31 Mar 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Can, your, governance, keep, pace, with, your, ambitions, risk, intelligence, the, agentic, era</media:keywords>
</item>

<item>
<title>AWS launches frontier agents for security testing and cloud operations</title>
<link>https://news.jatlink.uk/7694</link>
<guid>https://news.jatlink.uk/7694</guid>
<description><![CDATA[ I&#039;m excited to announce that AWS Security Agent on-demand penetration testing and AWS DevOps Agent are now generally available, representing a new class of AI capabilities we announced at re:Invent called frontier agents. These autonomous systems work independently to achieve goals, scale massively to tackle concurrent tasks, and run persistently for hours or days without constant human oversight. Together, these agents are changing the way we secure and operate software. In preview, customers and partners report that AWS Security Agent compresses penetration testing timelines from weeks to hours and the AWS DevOps Agent supports 3–5x faster incident resolution. ]]></description>
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<pubDate>Tue, 31 Mar 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, launches, frontier, agents, for, security, testing, and, cloud, operations</media:keywords>
</item>

<item>
<title>Deliver hyper&amp;personalized viewer experiences with an agentic AI movie assistant using Amazon Bedrock AgentCore and Amazon Nova Sonic 2.0</title>
<link>https://news.jatlink.uk/7597</link>
<guid>https://news.jatlink.uk/7597</guid>
<description><![CDATA[ In this post, we walk through two use cases that help enhance the user viewing experience using agentic AI tools and frameworks including Strands Agents SDK, Amazon Bedrock AgentCore, and Amazon Nova Sonic 2.0. This agentic AI system uses a Model Context Protocol (MCP) to deliver a personal entertainment concierge that understands user preferences through natural dialogue. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:28 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deliver, hyper-personalized, viewer, experiences, with, agentic, movie, assistant, using, Amazon, Bedrock, AgentCore, and, Amazon, Nova, Sonic, 2.0</media:keywords>
</item>

<item>
<title>How Ring scales global customer support with Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/7594</link>
<guid>https://news.jatlink.uk/7594</guid>
<description><![CDATA[ In this post, you&#039;ll learn how Ring implemented metadata-driven filtering for Region-specific content, separated content management into ingestion, evaluation and promotion workflows, and achieved cost savings while scaling up. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:27 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Ring, scales, global, customer, support, with, Amazon, Bedrock, Knowledge, Bases</media:keywords>
</item>

<item>
<title>Build a solar flare detection system on SageMaker AI LSTM networks and ESA STIX data</title>
<link>https://news.jatlink.uk/7596</link>
<guid>https://news.jatlink.uk/7596</guid>
<description><![CDATA[ In this post, we show you how to use Amazon SageMaker AI to build and deploy a deep learning model for detecting solar flares using data from the European Space Agency&#039;s STIX instrument. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:27 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, solar, flare, detection, system, SageMaker, LSTM, networks, and, ESA, STIX, data</media:keywords>
</item>

<item>
<title>Reimagine marketing at Volkswagen Group with generative AI</title>
<link>https://news.jatlink.uk/7595</link>
<guid>https://news.jatlink.uk/7595</guid>
<description><![CDATA[ In this post, we explore the challenges that Volkswagen Group faced in producing brand-compliant marketing assets at scale. We walk through how we built a generative AI solution that generates photorealistic vehicle images, validates technical accuracy at the component level, and helps enforce brand guideline compliance alignment across the ten brands. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:27 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reimagine, marketing, Volkswagen, Group, with, generative</media:keywords>
</item>

<item>
<title>Run Generative AI inference with Amazon Bedrock in Asia Pacific (New Zealand)</title>
<link>https://news.jatlink.uk/7274</link>
<guid>https://news.jatlink.uk/7274</guid>
<description><![CDATA[ Today, we’re excited to announce that Amazon Bedrock is now available in the Asia Pacific (New Zealand) Region (ap-southeast-6). Customers in New Zealand can now access Anthropic Claude models (Claude Opus 4.5, Opus 4.6, Sonnet 4.5, Sonnet 4.6, and Haiku 4.5) and Amazon (Nova 2 Lite) models directly in the Auckland Region with cross region inference. In this post, we explore how cross-Region inference works from the New Zealand Region, the models available through geographic and global routing, and how to get started with your first API call. We ]]></description>
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<pubDate>Fri, 27 Mar 2026 01:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, Generative, inference, with, Amazon, Bedrock, Asia, Pacific, New, Zealand</media:keywords>
</item>

<item>
<title>Accelerating LLM fine&amp;tuning with unstructured data using SageMaker Unified Studio and S3</title>
<link>https://news.jatlink.uk/7258</link>
<guid>https://news.jatlink.uk/7258</guid>
<description><![CDATA[ Last year, AWS announced an integration between Amazon SageMaker Unified Studio and Amazon S3 general purpose buckets. This integration makes it straightforward for teams to use unstructured data stored in Amazon Simple Storage Service (Amazon S3) for machine learning (ML) and data analytics use cases. In this post, we show how to integrate S3 general purpose buckets with Amazon SageMaker Catalog to fine-tune Llama 3.2 11B Vision Instruct for visual question answering (VQA) using Amazon SageMaker Unified Studio. ]]></description>
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<pubDate>Thu, 26 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, LLM, fine-tuning, with, unstructured, data, using, SageMaker, Unified, Studio, and</media:keywords>
</item>

<item>
<title>Introducing Amazon Polly Bidirectional Streaming: Real&amp;time speech synthesis for conversational AI</title>
<link>https://news.jatlink.uk/7259</link>
<guid>https://news.jatlink.uk/7259</guid>
<description><![CDATA[ Today, we’re excited to announce the new Bidirectional Streaming API for Amazon Polly, enabling streamlined real-time text-to-speech (TTS) synthesis where you can start sending text and receiving audio simultaneously. This new API is built for conversational AI applications that generate text or audio incrementally, like responses from large language models (LLMs), where users must begin synthesizing audio before the full text is available. ]]></description>
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<pubDate>Thu, 26 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Amazon, Polly, Bidirectional, Streaming:, Real-time, speech, synthesis, for, conversational</media:keywords>
</item>

<item>
<title>Building age&amp;responsive, context&amp;aware AI with Amazon Bedrock Guardrails</title>
<link>https://news.jatlink.uk/7257</link>
<guid>https://news.jatlink.uk/7257</guid>
<description><![CDATA[ In this post, we walk you through how to implement a fully automated, context-aware AI solution using a serverless architecture on AWS. This solution helps organizations looking to deploy responsible AI systems, align with compliance requirements for vulnerable populations, and help maintain appropriate and trustworthy AI responses across diverse user groups without compromising performance or governance. ]]></description>
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<pubDate>Thu, 26 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, age-responsive, context-aware, with, Amazon, Bedrock, Guardrails</media:keywords>
</item>

<item>
<title>Unlocking video insights at scale with Amazon Bedrock multimodal models</title>
<link>https://news.jatlink.uk/7149</link>
<guid>https://news.jatlink.uk/7149</guid>
<description><![CDATA[ In this post, we explore how the multimodal foundation models (FMs) of Amazon Bedrock enable scalable video understanding through three distinct architectural approaches. Each approach is designed for different use cases and cost-performance trade-offs. ]]></description>
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<pubDate>Wed, 25 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unlocking, video, insights, scale, with, Amazon, Bedrock, multimodal, models</media:keywords>
</item>

<item>
<title>Deploy voice agents with Pipecat and Amazon Bedrock AgentCore Runtime – Part 1</title>
<link>https://news.jatlink.uk/7150</link>
<guid>https://news.jatlink.uk/7150</guid>
<description><![CDATA[ In this series of posts, you will learn how streaming architectures help address these challenges using Pipecat voice agents on Amazon Bedrock AgentCore Runtime. In Part 1, you will learn how to deploy Pipecat voice agents on AgentCore Runtime using different network transport approaches including WebSockets, WebRTC and telephony integration, with practical deployment guidance and code samples. ]]></description>
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<pubDate>Wed, 25 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, voice, agents, with, Pipecat, and, Amazon, Bedrock, AgentCore, Runtime, –, Part</media:keywords>
</item>

<item>
<title>Reinforcement fine&amp;tuning on Amazon Bedrock with OpenAI&amp;Compatible APIs: a technical walkthrough</title>
<link>https://news.jatlink.uk/7151</link>
<guid>https://news.jatlink.uk/7151</guid>
<description><![CDATA[ In this post, we walk through the end-to-end workflow of using RFT on Amazon Bedrock with OpenAI-compatible APIs: from setting up authentication, to deploying a Lambda-based reward function, to kicking off a training job and running on-demand inference on your fine-tuned model. ]]></description>
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<pubDate>Wed, 25 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reinforcement, fine-tuning, Amazon, Bedrock, with, OpenAI-Compatible, APIs:, technical, walkthrough</media:keywords>
</item>

<item>
<title>Accelerating custom entity recognition with Claude tool use in Amazon Bedrock</title>
<link>https://news.jatlink.uk/7046</link>
<guid>https://news.jatlink.uk/7046</guid>
<description><![CDATA[ This post introduces Claude Tool use in Amazon Bedrock which uses the power of large language models (LLMs) to perform dynamic, adaptable entity recognition without extensive setup or training. ]]></description>
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<pubDate>Tue, 24 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, custom, entity, recognition, with, Claude, tool, use, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Deploy SageMaker AI inference endpoints with set GPU capacity using training plans</title>
<link>https://news.jatlink.uk/7045</link>
<guid>https://news.jatlink.uk/7045</guid>
<description><![CDATA[ In this post, we walk through how to search for available p-family GPU capacity, create a training plan reservation for inference, and deploy a SageMaker AI inference endpoint on that reserved capacity. We follow a data scientist&#039;s journey as they reserve capacity for model evaluation and manage the endpoint throughout the reservation lifecycle. ]]></description>
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<pubDate>Tue, 24 Mar 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, SageMaker, inference, endpoints, with, set, GPU, capacity, using, training, plans</media:keywords>
</item>

<item>
<title>Overcoming LLM hallucinations in regulated industries: Artificial Genius’s deterministic models on Amazon Nova</title>
<link>https://news.jatlink.uk/6915</link>
<guid>https://news.jatlink.uk/6915</guid>
<description><![CDATA[ In this post, we’re excited to showcase how AWS ISV Partner Artificial Genius is using Amazon SageMaker AI and Amazon Nova to deliver a solution that is probabilistic on input but deterministic on output, helping to enable safe, enterprise-grade adoption. ]]></description>
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<pubDate>Mon, 23 Mar 2026 17:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Overcoming, LLM, hallucinations, regulated, industries:, Artificial, Genius’s, deterministic, models, Amazon, Nova</media:keywords>
</item>

<item>
<title>How Reco transforms security alerts using Amazon Bedrock</title>
<link>https://news.jatlink.uk/6913</link>
<guid>https://news.jatlink.uk/6913</guid>
<description><![CDATA[ In this blog post, we show you how Reco implemented Amazon Bedrock to help transform security alerts and achieve significant improvements in incident response times. ]]></description>
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<pubDate>Mon, 23 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Reco, transforms, security, alerts, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Integrating Amazon Bedrock AgentCore with Slack</title>
<link>https://news.jatlink.uk/6914</link>
<guid>https://news.jatlink.uk/6914</guid>
<description><![CDATA[ In this post, we demonstrate how to build a Slack integration using AWS Cloud Development Kit (AWS CDK). You will learn how to deploy the infrastructure with three specialized AWS Lambda functions, configure event subscriptions properly to handle Slack&#039;s security requirements, and implement conversation management patterns that work for many agent use cases. ]]></description>
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<pubDate>Mon, 23 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Integrating, Amazon, Bedrock, AgentCore, with, Slack</media:keywords>
</item>

<item>
<title>Enforce data residency with Amazon Quick extensions for Microsoft Teams</title>
<link>https://news.jatlink.uk/6596</link>
<guid>https://news.jatlink.uk/6596</guid>
<description><![CDATA[ In this post, we will show you how to enforce data residency when deploying Amazon Quick Microsoft Teams extensions across multiple AWS Regions. You will learn how to configure multi-Region Amazon Quick extensions that automatically route users to AWS Region-appropriate resources, helping keep compliance with GDPR and other data sovereignty requirements. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enforce, data, residency, with, Amazon, Quick, extensions, for, Microsoft, Teams</media:keywords>
</item>

<item>
<title>Introducing V&amp;RAG: revolutionizing AI&amp;powered video production with Retrieval Augmented Generation</title>
<link>https://news.jatlink.uk/6594</link>
<guid>https://news.jatlink.uk/6594</guid>
<description><![CDATA[ This post introduces Video Retrieval-Augmented Generation (V-RAG), an approach to help improve video content creation. By combining retrieval augmented generation with advanced video AI models, V-RAG offers an efficient, and reliable solution for generating AI videos. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:09 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, V-RAG:, revolutionizing, AI-powered, video, production, with, Retrieval, Augmented, Generation</media:keywords>
</item>

<item>
<title>Enhanced metrics for Amazon SageMaker AI endpoints: deeper visibility for better performance</title>
<link>https://news.jatlink.uk/6595</link>
<guid>https://news.jatlink.uk/6595</guid>
<description><![CDATA[ SageMaker AI endpoints now support enhanced metrics with configurable publishing frequency. This launch provides the granular visibility needed to monitor, troubleshoot, and improve your production endpoints. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:09 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enhanced, metrics, for, Amazon, SageMaker, endpoints:, deeper, visibility, for, better, performance</media:keywords>
</item>

<item>
<title>Run NVIDIA Nemotron 3 Super on Amazon Bedrock</title>
<link>https://news.jatlink.uk/6592</link>
<guid>https://news.jatlink.uk/6592</guid>
<description><![CDATA[ This post explores the technical characteristics of the Nemotron 3 Super model and discusses potential application use cases. It also provides technical guidance to get started using this model for your generative AI applications within the Amazon Bedrock environment. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, NVIDIA, Nemotron, Super, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Use RAG for video generation using Amazon Bedrock and Amazon Nova Reel</title>
<link>https://news.jatlink.uk/6593</link>
<guid>https://news.jatlink.uk/6593</guid>
<description><![CDATA[ In this post, we explore our approach to video generation through VRAG, transforming natural language text prompts and images into grounded, high-quality videos. Through this fully automated solution, you can generate realistic, AI-powered video sequences from structured text and image inputs, streamlining the video creation process. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Use, RAG, for, video, generation, using, Amazon, Bedrock, and, Amazon, Nova, Reel</media:keywords>
</item>

<item>
<title>How Bark.com and AWS collaborated to build a scalable video generation solution</title>
<link>https://news.jatlink.uk/6465</link>
<guid>https://news.jatlink.uk/6465</guid>
<description><![CDATA[ Working with the AWS Generative AI Innovation Center, Bark developed an AI-powered content generation solution that demonstrated a substantial reduction in production time in experimental trials while improving content quality scores. In this post, we walk you through the technical architecture we built, the key design decisions that contributed to success, and the measurable results achieved, giving you a blueprint for implementing similar solutions. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Bark.com, and, AWS, collaborated, build, scalable, video, generation, solution</media:keywords>
</item>

<item>
<title>Migrate from Amazon Nova 1 to Amazon Nova 2 on Amazon Bedrock</title>
<link>https://news.jatlink.uk/6466</link>
<guid>https://news.jatlink.uk/6466</guid>
<description><![CDATA[ In this post, you will learn how to migrate from Nova 1 to Nova 2 on Amazon Bedrock. We cover model mapping, API changes, code examples using the Converse API, guidance on configuring new capabilities, and a summary of use cases. We conclude with a migration checklist to help you plan and execute your transition. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Migrate, from, Amazon, Nova, Amazon, Nova, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Evaluating AI agents for production: A practical guide to Strands Evals</title>
<link>https://news.jatlink.uk/6463</link>
<guid>https://news.jatlink.uk/6463</guid>
<description><![CDATA[ In this post, we show how to evaluate AI agents systematically using Strands Evals. We walk through the core concepts, built-in evaluators, multi-turn simulation capabilities and practical approaches and patterns for integration. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, agents, for, production:, practical, guide, Strands, Evals</media:keywords>
</item>

<item>
<title>Introducing Nova Forge SDK, a seamless way to customize Nova models for enterprise AI</title>
<link>https://news.jatlink.uk/6462</link>
<guid>https://news.jatlink.uk/6462</guid>
<description><![CDATA[ Today, we are launching Nova Forge SDK that makes LLM customization accessible, empowering teams to harness the full potential of language models without the challenges of dependency management, image selection, and recipe configuration and eventually lowering the barrier of entry. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Nova, Forge, SDK, seamless, way, customize, Nova, models, for, enterprise</media:keywords>
</item>

<item>
<title>Build an AI&amp;Powered A/B testing engine using Amazon Bedrock</title>
<link>https://news.jatlink.uk/6464</link>
<guid>https://news.jatlink.uk/6464</guid>
<description><![CDATA[ This post shows you how to build an AI-powered A/B testing engine using Amazon Bedrock, Amazon Elastic Container Service, Amazon DynamoDB, and the Model Context Protocol (MCP). The system improves traditional A/B testing by analyzing user context  to make smarter variant assignment decisions during the experiment. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-Powered, AB, testing, engine, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Kick off Nova customization experiments using Nova Forge SDK</title>
<link>https://news.jatlink.uk/6461</link>
<guid>https://news.jatlink.uk/6461</guid>
<description><![CDATA[ In this post, we walk you through the process of using the Nova Forge SDK to train an Amazon Nova model using Amazon SageMaker AI Training Jobs. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Kick, off, Nova, customization, experiments, using, Nova, Forge, SDK</media:keywords>
</item>

<item>
<title>AWS AI League: Atos fine&amp;tunes approach to AI education</title>
<link>https://news.jatlink.uk/6347</link>
<guid>https://news.jatlink.uk/6347</guid>
<description><![CDATA[ In this post, we’ll explore how Atos used the AWS AI League to help accelerate AI education across 400+ participants, highlight the tangible benefits of gamified, experiential learning, and share actionable insights you can apply to your own AI enablement programs. ]]></description>
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<pubDate>Tue, 17 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, League:, Atos, fine-tunes, approach, education</media:keywords>
</item>

<item>
<title>AWS and NVIDIA deepen strategic collaboration to accelerate AI from pilot to production</title>
<link>https://news.jatlink.uk/6259</link>
<guid>https://news.jatlink.uk/6259</guid>
<description><![CDATA[ Today at NVIDIA GTC 2026, AWS and NVIDIA announced an expanded collaboration with new technology integrations to support growing AI compute demand and help you build and run AI solutions that are production-ready. ]]></description>
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<pubDate>Mon, 16 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, and, NVIDIA, deepen, strategic, collaboration, accelerate, from, pilot, production</media:keywords>
</item>

<item>
<title>Agentic AI in the Enterprise Part 2: Guidance by Persona</title>
<link>https://news.jatlink.uk/6257</link>
<guid>https://news.jatlink.uk/6257</guid>
<description><![CDATA[ This is Part II of a two-part series from the AWS Generative AI Innovation Center. In Part II, we speak directly to the leaders who must turn that shared foundation into action. Each role carries a distinct set of responsibilities, risks, and leverage points. Whether you own a P&amp;L, run enterprise architecture, lead security, govern data, or manage compliance, this section is written in the language of your job—because that&#039;s where agentic AI either succeeds or quietly dies. ]]></description>
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<pubDate>Mon, 16 Mar 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, the, Enterprise, Part, Guidance, Persona</media:keywords>
</item>

<item>
<title>Introducing Disaggregated Inference on AWS powered by llm&amp;d</title>
<link>https://news.jatlink.uk/6258</link>
<guid>https://news.jatlink.uk/6258</guid>
<description><![CDATA[ In this blog post, we introduce the concepts behind next-generation inference capabilities, including disaggregated serving, intelligent request scheduling, and expert parallelism. We discuss their benefits and walk through how you can implement them on Amazon SageMaker HyperPod EKS to achieve significant improvements in inference performance, resource utilization, and operational efficiency. ]]></description>
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<pubDate>Mon, 16 Mar 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Disaggregated, Inference, AWS powered, llm-d</media:keywords>
</item>

<item>
<title>How Workhuman built multi&amp;tenant self&amp;service reporting using Amazon Quick Sight embedded dashboards</title>
<link>https://news.jatlink.uk/6238</link>
<guid>https://news.jatlink.uk/6238</guid>
<description><![CDATA[ This post explores how Workhuman transformed their analytics delivery model and the key lessons learned from their implementation. We go through their architecture approach, implementation strategy, and the business outcomes they achieved—providing you with a practical blueprint for adding embedded analytics to your own software as a service (SaaS) applications. ]]></description>
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<pubDate>Mon, 16 Mar 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Workhuman, built, multi-tenant, self-service, reporting, using, Amazon, Quick, Sight, embedded, dashboards</media:keywords>
</item>

<item>
<title>Build an offline feature store using Amazon SageMaker Unified Studio and SageMaker Catalog</title>
<link>https://news.jatlink.uk/6239</link>
<guid>https://news.jatlink.uk/6239</guid>
<description><![CDATA[ This blog post provides step-by-step guidance on implementing an offline feature store using SageMaker Catalog within a SageMaker Unified Studio domain. By adopting a publish-subscribe pattern, data producers can use this solution to publish curated, versioned feature tables—while data consumers can securely discover, subscribe to, and reuse them for model development. ]]></description>
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<pubDate>Mon, 16 Mar 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, offline, feature, store, using, Amazon, SageMaker, Unified, Studio, and, SageMaker, Catalog</media:keywords>
</item>

<item>
<title>P&amp;EAGLE: Faster LLM inference with Parallel Speculative Decoding in vLLM</title>
<link>https://news.jatlink.uk/6078</link>
<guid>https://news.jatlink.uk/6078</guid>
<description><![CDATA[ In this post, we explain how P-EAGLE works, how we integrated it into vLLM starting from v0.16.0 (PR#32887), and how to serve it with our pre-trained checkpoints. ]]></description>
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<pubDate>Fri, 13 Mar 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>P-EAGLE:, Faster, LLM, inference, with, Parallel, Speculative, Decoding, vLLM</media:keywords>
</item>

<item>
<title>Improve operational visibility for inference workloads on Amazon Bedrock with new CloudWatch metrics for TTFT and Estimated Quota Consumption</title>
<link>https://news.jatlink.uk/6015</link>
<guid>https://news.jatlink.uk/6015</guid>
<description><![CDATA[ Today, we’re announcing two new Amazon CloudWatch metrics for Amazon Bedrock, TimeToFirstToken and EstimatedTPMQuotaUsage. In this post, we cover how these work and how to set alarms, establish baselines, and proactively manage capacity using them. ]]></description>
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<pubDate>Fri, 13 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Improve, operational, visibility, for, inference, workloads, Amazon, Bedrock, with, new, CloudWatch, metrics, for, TTFT, and, Estimated, Quota, Consumption</media:keywords>
</item>

<item>
<title>Secure AI agents with Policy in Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/6016</link>
<guid>https://news.jatlink.uk/6016</guid>
<description><![CDATA[ In this post, you will understand how Policy in Amazon Bedrock AgentCore creates a deterministic enforcement layer that operates independently of the agent&#039;s own reasoning. You will learn how to turn natural language descriptions of your business rules into Cedar policies, then use those policies to enforce fine-grained, identity-aware controls so that agents only access the tools and data that their users are authorized to use. You will also see how to apply Policy through AgentCore Gateway, intercepting and evaluating every agent-to-tool request at runtime. ]]></description>
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<pubDate>Fri, 13 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Secure, agents, with, Policy, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Fine&amp;tuning NVIDIA Nemotron Speech ASR on Amazon EC2 for domain adaptation</title>
<link>https://news.jatlink.uk/5982</link>
<guid>https://news.jatlink.uk/5982</guid>
<description><![CDATA[ In this post, we explore how to fine-tune a leaderboard-topping, NVIDIA Nemotron Speech Automatic Speech Recognition (ASR) model; Parakeet TDT 0.6B V2. Using synthetic speech data to achieve superior transcription results for specialised applications, we&#039;ll walk through an end-to-end workflow that combines AWS infrastructure with the following popular open-source frameworks. ]]></description>
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<pubDate>Thu, 12 Mar 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Fine-tuning, NVIDIA, Nemotron, Speech, ASR, Amazon, EC2, for, domain, adaptation</media:keywords>
</item>

<item>
<title>Multimodal embeddings at scale: AI data lake for media and entertainment workloads</title>
<link>https://news.jatlink.uk/5981</link>
<guid>https://news.jatlink.uk/5981</guid>
<description><![CDATA[ This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon Nova models and Amazon OpenSearch Service. You will learn how to move beyond manual tagging and keyword-based searches to enable semantic search that captures the full richness of video content. ]]></description>
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<pubDate>Thu, 12 Mar 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Multimodal, embeddings, scale:, data, lake, for, media, and, entertainment, workloads</media:keywords>
</item>

<item>
<title>Operationalizing Agentic AI Part 1: A Stakeholder’s Guide</title>
<link>https://news.jatlink.uk/5928</link>
<guid>https://news.jatlink.uk/5928</guid>
<description><![CDATA[ The AWS Generative AI Innovation Center has helped 1,000+ customers move AI into production, delivering millions in documented productivity gains. In this post, we share guidance for leaders across the C-suite: CTOs, CISOs, CDOs, and Chief Data Science/AI officers, as well as business owners and compliance leads. ]]></description>
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<pubDate>Wed, 11 Mar 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Operationalizing, Agentic, Part, Stakeholder’s, Guide</media:keywords>
</item>

<item>
<title>Accelerate custom LLM deployment: Fine&amp;tune with Oumi and deploy to Amazon Bedrock</title>
<link>https://news.jatlink.uk/5826</link>
<guid>https://news.jatlink.uk/5826</guid>
<description><![CDATA[ In this post, we show how to fine-tune a Llama model using Oumi on Amazon EC2 (with the option to create synthetic data using Oumi), store artifacts in Amazon S3, and deploy to Amazon Bedrock using Custom Model Import for managed inference. ]]></description>
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<pubDate>Tue, 10 Mar 2026 16:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, custom, LLM, deployment:, Fine-tune, with, Oumi, and, deploy, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Access Anthropic Claude models in India on Amazon Bedrock with Global cross&amp;Region inference</title>
<link>https://news.jatlink.uk/5765</link>
<guid>https://news.jatlink.uk/5765</guid>
<description><![CDATA[ In this post, you will discover how to use Amazon Bedrock&#039;s Global cross-Region Inference for Claude models in India. We will guide you through the capabilities of each Claude model variant and how to get started with a code example to help you start building generative AI applications immediately. ]]></description>
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<pubDate>Mon, 09 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Access, Anthropic, Claude, models, India, Amazon, Bedrock, with, Global, cross-Region, inference</media:keywords>
</item>

<item>
<title>Run NVIDIA Nemotron 3 Nano as a fully managed serverless model on Amazon Bedrock</title>
<link>https://news.jatlink.uk/5764</link>
<guid>https://news.jatlink.uk/5764</guid>
<description><![CDATA[ We are excited to announce that NVIDIA’s Nemotron 3 Nano is now available as a fully managed and serverless model in Amazon Bedrock. This follows our earlier announcement at AWS re:Invent supporting NVIDIA Nemotron 2 Nano 9B and NVIDIA Nemotron 2 Nano VL 12B models. This post explores the technical characteristics of the NVIDIA Nemotron 3 Nano model and discusses potential application use cases. Additionally, it provides technical guidance to help you get started using this model for your generative AI applications within the Amazon Bedrock environment. ]]></description>
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<pubDate>Mon, 09 Mar 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, NVIDIA, Nemotron, Nano, fully, managed, serverless, model, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Drive organizational growth with Amazon Lex multi&amp;developer CI/CD pipeline</title>
<link>https://news.jatlink.uk/5491</link>
<guid>https://news.jatlink.uk/5491</guid>
<description><![CDATA[ In this post, we walk through a multi-developer CI/CD pipeline for Amazon Lex that enables isolated development environments, automated testing, and streamlined deployments. We show you how to set up the solution and share real-world results from teams using this approach. ]]></description>
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<pubDate>Thu, 05 Mar 2026 17:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Drive, organizational, growth, with, Amazon, Lex, multi-developer, CICD, pipeline</media:keywords>
</item>

<item>
<title>Building custom model provider for Strands Agents with LLMs hosted on SageMaker AI endpoints</title>
<link>https://news.jatlink.uk/5492</link>
<guid>https://news.jatlink.uk/5492</guid>
<description><![CDATA[ This post demonstrates how to build custom model parsers for Strands agents when working with LLMs hosted on SageMaker that don&#039;t natively support the Bedrock Messages API format. We&#039;ll walk through deploying Llama 3.1 with SGLang on SageMaker using awslabs/ml-container-creator, then implementing a custom parser to integrate it with Strands agents. ]]></description>
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<pubDate>Thu, 05 Mar 2026 17:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, custom, model, provider, for, Strands, Agents, with, LLMs, hosted, SageMaker, endpoints</media:keywords>
</item>

<item>
<title>Embed Amazon Quick Suite chat agents in enterprise applications</title>
<link>https://news.jatlink.uk/5447</link>
<guid>https://news.jatlink.uk/5447</guid>
<description><![CDATA[ Organizations find it challenging to implement a secure embedded chat in their applications and can require weeks of development to build authentication, token validation, domain security, and global distribution infrastructure. In this post, we show you how to solve this with a one-click deployment solution to embed the chat agents using the Quick Suite Embedding SDK in enterprise portals. ]]></description>
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<pubDate>Thu, 05 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Embed, Amazon, Quick, Suite, chat, agents, enterprise, applications</media:keywords>
</item>

<item>
<title>Unlock powerful call center analytics with Amazon Nova foundation models</title>
<link>https://news.jatlink.uk/5448</link>
<guid>https://news.jatlink.uk/5448</guid>
<description><![CDATA[ In this post, we discuss how Amazon Nova demonstrates capabilities in conversational analytics, call classification, and other use cases often relevant to contact center solutions. We examine these capabilities for both single-call and multi-call analytics use cases. ]]></description>
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<pubDate>Thu, 05 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unlock, powerful, call, center, analytics, with, Amazon, Nova, foundation, models</media:keywords>
</item>

<item>
<title>How Ricoh built a scalable intelligent document processing solution on AWS</title>
<link>https://news.jatlink.uk/5432</link>
<guid>https://news.jatlink.uk/5432</guid>
<description><![CDATA[ This post explores how Ricoh built a standardized, multi-tenant solution for automated document classification and extraction using the AWS GenAI IDP Accelerator as a foundation, transforming their document processing from a custom-engineering bottleneck into a scalable, repeatable service. ]]></description>
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<pubDate>Wed, 04 Mar 2026 21:00:12 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Ricoh, built, scalable, intelligent, document, processing, solution, AWS</media:keywords>
</item>

<item>
<title>Building a scalable virtual try&amp;on solution using Amazon Nova on AWS: part 1</title>
<link>https://news.jatlink.uk/5339</link>
<guid>https://news.jatlink.uk/5339</guid>
<description><![CDATA[ In this post, we explore the virtual try-on capability now available in Amazon Nova Canvas, including sample code to get started quickly and tips to help get the best outputs. ]]></description>
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<pubDate>Tue, 03 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, scalable, virtual, try-on, solution, using, Amazon, Nova, AWS:, part</media:keywords>
</item>

<item>
<title>How Lendi revamped the refinance journey for its customers using agentic AI in 16 weeks using Amazon Bedrock</title>
<link>https://news.jatlink.uk/5340</link>
<guid>https://news.jatlink.uk/5340</guid>
<description><![CDATA[ This post details how Lendi Group built their AI-powered Home Loan Guardian using Amazon Bedrock, the challenges they faced, the architecture they implemented, and the significant business outcomes they’ve achieved. Their journey offers valuable insights for organizations that want to use generative AI to transform customer experiences while maintaining the human touch that builds trust and loyalty. ]]></description>
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<pubDate>Tue, 03 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Lendi, revamped, the, refinance, journey, for, its, customers, using, agentic, weeks, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Tines enhances security analysis with Amazon Quick Suite</title>
<link>https://news.jatlink.uk/5341</link>
<guid>https://news.jatlink.uk/5341</guid>
<description><![CDATA[ In this post, we show you how to connect Quick Suite with Tines to securely retrieve, analyze, and visualize enterprise data from any security or IT system. We walk through an example that uses a MCP server in Tines to retrieve data from various tools, such as AWS CloudTrail, Okta, and VirusTotal, to remediate security events using Quick Suite. ]]></description>
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<pubDate>Tue, 03 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Tines, enhances, security, analysis, with, Amazon, Quick, Suite</media:keywords>
</item>

<item>
<title>Build safe generative AI applications like a Pro: Best Practices with Amazon Bedrock Guardrails</title>
<link>https://news.jatlink.uk/5284</link>
<guid>https://news.jatlink.uk/5284</guid>
<description><![CDATA[ In this post, we will show you how to configure Amazon Bedrock Guardrails for efficient performance, implement best practices to protect your applications, and monitor your deployment effectively to maintain the right balance between safety and user experience. ]]></description>
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<pubDate>Mon, 02 Mar 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, safe, generative, applications, like, Pro:, Best, Practices, with, Amazon, Bedrock, Guardrails</media:keywords>
</item>

<item>
<title>Build a serverless conversational AI agent using Claude with LangGraph and managed MLflow on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/5283</link>
<guid>https://news.jatlink.uk/5283</guid>
<description><![CDATA[ This post explores how to build an intelligent conversational agent using Amazon Bedrock, LangGraph, and managed MLflow on Amazon SageMaker AI. ]]></description>
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<pubDate>Mon, 02 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, serverless, conversational, agent, using, Claude, with, LangGraph, and, managed, MLflow, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Building specialized AI without sacrificing intelligence: Nova Forge data mixing in action</title>
<link>https://news.jatlink.uk/5282</link>
<guid>https://news.jatlink.uk/5282</guid>
<description><![CDATA[ In this post, we share results from the AWS China Applied Science team&#039;s comprehensive evaluation of Nova Forge using a challenging Voice of Customer (VOC) classification task, benchmarked against open-source models. ]]></description>
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<pubDate>Mon, 02 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, specialized, without, sacrificing, intelligence: Nova, Forge, data, mixing, action</media:keywords>
</item>

<item>
<title>Learnings from COBOL modernization in the real world</title>
<link>https://news.jatlink.uk/5022</link>
<guid>https://news.jatlink.uk/5022</guid>
<description><![CDATA[ Delivering successful COBOL modernization requires a solution that can reverse engineer deterministically, produce validated and traceable specs, and help those specs flow into any AI-powered coding assistant for the forward engineering. A successful modernization requires both reverse engineering and forward engineering. Learn more about COBOL in this post. ]]></description>
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<pubDate>Thu, 26 Feb 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Learnings, from, COBOL, modernization, the, real, world</media:keywords>
</item>

<item>
<title>Reinforcement fine&amp;tuning for Amazon Nova: Teaching AI through feedback</title>
<link>https://news.jatlink.uk/5023</link>
<guid>https://news.jatlink.uk/5023</guid>
<description><![CDATA[ In this post, we explore reinforcement fine-tuning (RFT) for Amazon Nova models, which can be a powerful customization technique that learns through evaluation rather than imitation. We&#039;ll cover how RFT works, when to use it versus supervised fine-tuning, real-world applications from code generation to customer service, and implementation options ranging from fully managed Amazon Bedrock to multi-turn agentic workflows with Nova Forge. You&#039;ll also learn practical guidance on data preparation, reward function design, and best practices for achieving optimal results. ]]></description>
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<pubDate>Thu, 26 Feb 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reinforcement, fine-tuning, for, Amazon, Nova:, Teaching, through, feedback</media:keywords>
</item>

<item>
<title>Large model inference container – latest capabilities and performance enhancements</title>
<link>https://news.jatlink.uk/5024</link>
<guid>https://news.jatlink.uk/5024</guid>
<description><![CDATA[ AWS recently released significant updates to the Large Model Inference (LMI) container, delivering comprehensive performance improvements, expanded model support, and streamlined deployment capabilities for customers hosting LLMs on AWS. These releases focus on reducing operational complexity while delivering measurable performance gains across popular model architectures. ]]></description>
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<pubDate>Thu, 26 Feb 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Large, model, inference, container, –, latest, capabilities, and, performance, enhancements</media:keywords>
</item>

<item>
<title>Efficiently serve dozens of fine&amp;tuned models with vLLM on Amazon SageMaker AI and Amazon Bedrock</title>
<link>https://news.jatlink.uk/4942</link>
<guid>https://news.jatlink.uk/4942</guid>
<description><![CDATA[ In this post, we explain how we implemented multi-LoRA inference for Mixture of Experts (MoE) models in vLLM, describe the kernel-level optimizations we performed, and show you how you can benefit from this work. We use GPT-OSS 20B as our primary example throughout this post. ]]></description>
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<pubDate>Wed, 25 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Efficiently, serve, dozens, fine-tuned, models, with, vLLM, Amazon, SageMaker, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Building intelligent event agents using Amazon Bedrock AgentCore and Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/4941</link>
<guid>https://news.jatlink.uk/4941</guid>
<description><![CDATA[ This post demonstrates how to quickly deploy a production-ready event assistant using the components of Amazon Bedrock AgentCore. We&#039;ll build an intelligent companion that remembers attendee preferences and builds personalized experiences over time, while Amazon Bedrock AgentCore handles the heavy lifting of production deployment: Amazon Bedrock AgentCore Memory for maintaining both conversation context and long-term preferences without custom storage solutions, Amazon Bedrock AgentCore Identity for secure multi-IDP authentication, and Amazon Bedrock AgentCore Runtime for serverless scaling and session isolation. We will also use Amazon Bedrock Knowledge Bases for managed RAG and event data retrieval. ]]></description>
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<pubDate>Wed, 25 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, intelligent, event, agents, using, Amazon, Bedrock, AgentCore, and, Amazon, Bedrock, Knowledge, Bases</media:keywords>
</item>

<item>
<title>Build an intelligent photo search using Amazon Rekognition, Amazon Neptune, and Amazon Bedrock</title>
<link>https://news.jatlink.uk/4864</link>
<guid>https://news.jatlink.uk/4864</guid>
<description><![CDATA[ In this post, we show you how to build a comprehensive photo search system using the AWS Cloud Development Kit (AWS CDK) that integrates Amazon Rekognition for face and object detection, Amazon Neptune for relationship mapping, and Amazon Bedrock for AI-powered captioning. ]]></description>
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<pubDate>Tue, 24 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, intelligent, photo, search, using, Amazon, Rekognition, Amazon, Neptune, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Introducing Amazon Bedrock global cross&amp;Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)</title>
<link>https://news.jatlink.uk/4842</link>
<guid>https://news.jatlink.uk/4842</guid>
<description><![CDATA[ We’re excited to announce the availability of Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, and Claude Haiku 4.5 through Amazon Bedrock global cross-Region inference for customers operating in the Middle East. In this post, we guide you through the capabilities of each Anthropic Claude model variant, the key advantages of global cross-Region inference including improved resilience, real-world use cases you can implement, and a code example to help you start building generative AI applications immediately. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Amazon, Bedrock, global, cross-Region, inference, for, Anthropic’s, Claude, models, the, Middle, East, Regions, UAE, and, Bahrain</media:keywords>
</item>

<item>
<title>Generate structured output from LLMs with Dottxt Outlines in AWS</title>
<link>https://news.jatlink.uk/4840</link>
<guid>https://news.jatlink.uk/4840</guid>
<description><![CDATA[ This post explores the implementation of Dottxt’s Outlines framework as a practical approach to implementing structured outputs using AWS Marketplace in Amazon SageMaker. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Generate, structured, output, from, LLMs, with, Dottxt, Outlines, AWS</media:keywords>
</item>

<item>
<title>Train CodeFu&amp;7B with veRL and Ray on Amazon SageMaker Training jobs</title>
<link>https://news.jatlink.uk/4839</link>
<guid>https://news.jatlink.uk/4839</guid>
<description><![CDATA[ In this post, we demonstrate how to train CodeFu-7B, a specialized 7-billion parameter model for competitive programming, using Group Relative Policy Optimization (GRPO) with veRL, a flexible and efficient training library for large language models (LLMs) that enables straightforward extension of diverse RL algorithms and seamless integration with existing LLM infrastructure, within a distributed Ray cluster managed by SageMaker training jobs. We walk through the complete implementation, covering data preparation, distributed training setup, and comprehensive observability, showcasing how this unified approach delivers both computational scale and developer experience for sophisticated RL training workloads. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Train, CodeFu-7B, with, veRL, and, Ray, Amazon, SageMaker, Training, jobs</media:keywords>
</item>

<item>
<title>Global cross&amp;Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan</title>
<link>https://news.jatlink.uk/4841</link>
<guid>https://news.jatlink.uk/4841</guid>
<description><![CDATA[ In this post, we are exciting to announce availability of Global CRIS for customers in Thailand, Malaysia, Singapore, Indonesia, and Taiwan and give a walkthrough of technical implementation steps, and cover quota management best practices to maximize the value of your AI Inference deployments. We also provide guidance on best practices for production deployments. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Global, cross-Region, inference, for, latest, Anthropic, Claude, Opus, Sonnet, and, Haiku, models, Amazon, Bedrock, Thailand, Malaysia, Singapore, Indonesia, and, Taiwan</media:keywords>
</item>

<item>
<title>Scaling data annotation using vision&amp;language models to power physical AI systems</title>
<link>https://news.jatlink.uk/4803</link>
<guid>https://news.jatlink.uk/4803</guid>
<description><![CDATA[ In this post, we examine how Bedrock Robotics tackles this challenge. By joining the AWS Physical AI Fellowship, the startup partnered with the AWS Generative AI Innovation Center to apply vision-language models that analyze construction video footage, extract operational details, and generate labeled training datasets at scale, to improve data preparation for autonomous construction equipment. ]]></description>
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<pubDate>Tue, 24 Feb 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, data, annotation, using, vision-language, models, power, physical, systems</media:keywords>
</item>

<item>
<title>Accelerating AI model production at Hexagon with Amazon SageMaker HyperPod</title>
<link>https://news.jatlink.uk/4789</link>
<guid>https://news.jatlink.uk/4789</guid>
<description><![CDATA[ In this blog post, we demonstrate how Hexagon collaborated with Amazon Web Services to scale their AI model production by pretraining state-of-the-art segmentation models, using the model training infrastructure of Amazon SageMaker HyperPod. ]]></description>
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<pubDate>Mon, 23 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, model, production, Hexagon, with, Amazon, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>How Sonrai uses Amazon SageMaker AI to accelerate precision medicine trials</title>
<link>https://news.jatlink.uk/4788</link>
<guid>https://news.jatlink.uk/4788</guid>
<description><![CDATA[ In this post, we explore how Sonrai, a life sciences AI company, partnered with AWS to build a robust MLOps framework using Amazon SageMaker AI that addresses these challenges while maintaining the traceability and reproducibility required in regulated environments. ]]></description>
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<pubDate>Mon, 23 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Sonrai, uses, Amazon, SageMaker, accelerate, precision, medicine, trials</media:keywords>
</item>

<item>
<title>Agentic AI with multi&amp;model framework using Hugging Face smolagents on AWS</title>
<link>https://news.jatlink.uk/4763</link>
<guid>https://news.jatlink.uk/4763</guid>
<description><![CDATA[ Hugging Face smolagents is an open source Python library designed to make it straightforward to build and run agents using a few lines of code. We will show you how to build an agentic AI solution by integrating Hugging Face smolagents with Amazon Web Services (AWS) managed services. You&#039;ll learn how to deploy a healthcare AI agent that demonstrates multi-model deployment options, vector-enhanced knowledge retrieval, and clinical decision support capabilities. ]]></description>
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<pubDate>Mon, 23 Feb 2026 16:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, with, multi-model, framework, using, Hugging, Face, smolagents, AWS</media:keywords>
</item>

<item>
<title>Amazon SageMaker AI in 2025, a year in review part 2: Improved observability and enhanced features for SageMaker AI model customization and hosting</title>
<link>https://news.jatlink.uk/4621</link>
<guid>https://news.jatlink.uk/4621</guid>
<description><![CDATA[ In 2025, Amazon SageMaker AI made several improvements designed to help you train, tune, and host generative AI workloads. In Part 1 of this series, we discussed Flexible Training Plans and price performance improvements made to inference components. In this post, we discuss enhancements made to observability, model customization, and model hosting. These improvements facilitate a whole new class of customer use cases to be hosted on SageMaker AI. ]]></description>
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<pubDate>Fri, 20 Feb 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, 2025, year, review, part, Improved, observability, and, enhanced, features, for, SageMaker, model, customization, and, hosting</media:keywords>
</item>

<item>
<title>Amazon SageMaker AI in 2025, a year in review part 1: Flexible Training Plans and improvements to price performance for inference workloads</title>
<link>https://news.jatlink.uk/4620</link>
<guid>https://news.jatlink.uk/4620</guid>
<description><![CDATA[ In 2025, Amazon SageMaker AI saw dramatic improvements to core infrastructure offerings along four dimensions: capacity, price performance, observability, and usability. In this series of posts, we discuss these various improvements and their benefits. In Part 1, we discuss capacity improvements with the launch of Flexible Training Plans. We also describe improvements to price performance for inference workloads. In Part 2, we discuss enhancements made to observability, model customization, and model hosting. ]]></description>
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<pubDate>Fri, 20 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, 2025, year, review, part, Flexible, Training, Plans, and, improvements, price, performance, for, inference, workloads</media:keywords>
</item>

<item>
<title>Integrate external tools with Amazon Quick Agents using Model Context Protocol (MCP)</title>
<link>https://news.jatlink.uk/4598</link>
<guid>https://news.jatlink.uk/4598</guid>
<description><![CDATA[ In this post, you’ll use a six-step checklist to build a new MCP server or validate and adjust an existing MCP server for Amazon Quick integration. The Amazon Quick User Guide describes the MCP client behavior and constraints. This is a “How to” guide for detailed implementation required by 3P partners to integrate with Amazon Quick with MCP. ]]></description>
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<pubDate>Fri, 20 Feb 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Integrate, external, tools, with, Amazon, Quick, Agents, using, Model, Context, Protocol, MCP</media:keywords>
</item>

<item>
<title>Amazon Quick now supports key pair authentication to Snowflake data source</title>
<link>https://news.jatlink.uk/4560</link>
<guid>https://news.jatlink.uk/4560</guid>
<description><![CDATA[ In this blog post, we will guide you through establishing data source connectivity between Amazon Quick Sight and Snowflake through secure key pair authentication. ]]></description>
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<pubDate>Fri, 20 Feb 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, now, supports, key, pair, authentication, Snowflake, data, source</media:keywords>
</item>

<item>
<title>Amazon Quick Suite now supports key pair authentication to Snowflake data source</title>
<link>https://news.jatlink.uk/4526</link>
<guid>https://news.jatlink.uk/4526</guid>
<description><![CDATA[ In this blog post, we will guide you through establishing data source connectivity between Amazon Quick Sight and Snowflake through secure key pair authentication. ]]></description>
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<pubDate>Thu, 19 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, Suite, now, supports, key, pair, authentication, Snowflake, data, source</media:keywords>
</item>

<item>
<title>Build AI workflows on Amazon EKS with Union.ai and Flyte</title>
<link>https://news.jatlink.uk/4525</link>
<guid>https://news.jatlink.uk/4525</guid>
<description><![CDATA[ In this post, we explain how you can use the Flyte Python SDK to orchestrate and scale AI/ML workflows. We explore how the Union.ai 2.0 system enables deployment of Flyte on Amazon Elastic Kubernetes Service (Amazon EKS), integrating seamlessly with AWS services like Amazon Simple Storage Service (Amazon S3), Amazon Aurora, AWS Identity and Access Management (IAM), and Amazon CloudWatch. We explore the solution through an AI workflow example, using the new Amazon S3 Vectors service. ]]></description>
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<pubDate>Thu, 19 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, workflows, Amazon, EKS, with, Union.ai, and, Flyte</media:keywords>
</item>

<item>
<title>Build unified intelligence with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/4479</link>
<guid>https://news.jatlink.uk/4479</guid>
<description><![CDATA[ In this post, we demonstrate how to build unified intelligence systems using Amazon Bedrock AgentCore through our real-world implementation of the Customer Agent and Knowledge Engine (CAKE). ]]></description>
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<pubDate>Thu, 19 Feb 2026 00:00:15 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, unified, intelligence, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Evaluating AI agents: Real&amp;world lessons from building agentic systems at Amazon</title>
<link>https://news.jatlink.uk/4463</link>
<guid>https://news.jatlink.uk/4463</guid>
<description><![CDATA[ In this post, we present a comprehensive evaluation framework for Amazon agentic AI systems that addresses the complexity of agentic AI applications at Amazon through two core components: a generic evaluation workflow that standardizes assessment procedures across diverse agent implementations, and an agent evaluation library that provides systematic measurements and metrics in Amazon Bedrock AgentCore Evaluations, along with Amazon use case-specific evaluation approaches and metrics.  ]]></description>
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<pubDate>Wed, 18 Feb 2026 20:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, agents:, Real-world, lessons, from, building, agentic, systems, Amazon</media:keywords>
</item>

<item>
<title>Customize AI agent browsing with proxies, profiles, and extensions in Amazon Bedrock AgentCore Browser</title>
<link>https://news.jatlink.uk/4172</link>
<guid>https://news.jatlink.uk/4172</guid>
<description><![CDATA[ Today, we are announcing three new capabilities that address these requirements: proxy configuration, browser profiles, and browser extensions. Together, these features give you fine-grained control over how your AI agents interact with the web. This post will walk through each capability with configuration examples and practical use cases to help you get started. ]]></description>
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<pubDate>Sat, 14 Feb 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Customize, agent, browsing, with, proxies, profiles, and, extensions, Amazon, Bedrock, AgentCore, Browser</media:keywords>
</item>

<item>
<title>AI meets HR: Transforming talent acquisition with Amazon Bedrock</title>
<link>https://news.jatlink.uk/4078</link>
<guid>https://news.jatlink.uk/4078</guid>
<description><![CDATA[ In this post, we show how to create an AI-powered recruitment system using Amazon Bedrock, Amazon Bedrock Knowledge Bases, AWS Lambda, and other AWS services to enhance job description creation, candidate communication, and interview preparation while maintaining human oversight. ]]></description>
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<pubDate>Thu, 12 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>meets, HR:, Transforming, talent, acquisition, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Build long&amp;running MCP servers on Amazon Bedrock AgentCore with Strands Agents integration</title>
<link>https://news.jatlink.uk/4079</link>
<guid>https://news.jatlink.uk/4079</guid>
<description><![CDATA[ In this post, we provide you with a comprehensive approach to achieve this. First, we introduce a context message strategy that maintains continuous communication between servers and clients during extended operations. Next, we develop an asynchronous task management framework that allows your AI agents to initiate long-running processes without blocking other operations. Finally, we demonstrate how to bring these strategies together with Amazon Bedrock AgentCore and Strands Agents to build production-ready AI agents that can handle complex, time-intensive operations reliably. ]]></description>
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<pubDate>Thu, 12 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, long-running, MCP, servers, Amazon, Bedrock, AgentCore, with, Strands, Agents, integration</media:keywords>
</item>

<item>
<title>NVIDIA Nemotron 3 Nano 30B MoE model is now available in Amazon SageMaker JumpStart</title>
<link>https://news.jatlink.uk/4004</link>
<guid>https://news.jatlink.uk/4004</guid>
<description><![CDATA[ Today we’re excited to announce that the NVIDIA Nemotron 3 Nano 30B model with  3B active parameters is now generally available in the Amazon SageMaker JumpStart model catalog. You can accelerate innovation and deliver tangible business value with Nemotron 3 Nano on Amazon Web Services (AWS) without having to manage model deployment complexities. You can power your generative AI applications with Nemotron capabilities using the managed deployment capabilities offered by SageMaker JumpStart. ]]></description>
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<pubDate>Wed, 11 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Nemotron, Nano, 30B, MoE, model, now, available, Amazon, SageMaker, JumpStart</media:keywords>
</item>

<item>
<title>Mastering Amazon Bedrock throttling and service availability: A comprehensive guide</title>
<link>https://news.jatlink.uk/3981</link>
<guid>https://news.jatlink.uk/3981</guid>
<description><![CDATA[ This post shows you how to implement robust error handling strategies that can help improve application reliability and user experience when using Amazon Bedrock. We&#039;ll dive deep into strategies for optimizing performances for the application with these errors. Whether this is for a fairly new application or matured AI application, in this post you will be able to find the practical guidelines to operate with on these errors. ]]></description>
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<pubDate>Wed, 11 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Mastering, Amazon, Bedrock, throttling, and, service, availability:, comprehensive, guide</media:keywords>
</item>

<item>
<title>Swann provides Generative AI to millions of IoT Devices using Amazon Bedrock</title>
<link>https://news.jatlink.uk/3982</link>
<guid>https://news.jatlink.uk/3982</guid>
<description><![CDATA[ This post shows you how to implement intelligent notification filtering using Amazon Bedrock and its gen-AI capabilities. You&#039;ll learn model selection strategies, cost optimization techniques, and architectural patterns for deploying gen-AI at IoT scale, based on Swann Communications deployment across millions of devices. ]]></description>
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<pubDate>Wed, 11 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Swann, provides, Generative, millions, IoT, Devices, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How LinqAlpha assesses investment theses using Devil’s Advocate on Amazon Bedrock</title>
<link>https://news.jatlink.uk/3983</link>
<guid>https://news.jatlink.uk/3983</guid>
<description><![CDATA[ LinqAlpha is a Boston-based multi-agent AI system built specifically for institutional investors. The system supports and streamlines agentic workflows across company screening, primer generation, stock price catalyst mapping, and now, pressure-testing investment ideas through a new AI agent called Devil’s Advocate. In this post, we share how LinqAlpha uses Amazon Bedrock to build and scale Devil’s Advocate. ]]></description>
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<pubDate>Wed, 11 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, LinqAlpha, assesses, investment, theses, using, Devil’s, Advocate, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Amazon uses Amazon Nova models to automate operational readiness testing for new fulfillment centers</title>
<link>https://news.jatlink.uk/3930</link>
<guid>https://news.jatlink.uk/3930</guid>
<description><![CDATA[ In this post, we discuss how Amazon Nova in Amazon Bedrock can be used to implement an AI-powered image recognition solution that automates the detection and validation of module components, significantly reducing manual verification efforts and improving accuracy. ]]></description>
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<pubDate>Tue, 10 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Amazon, uses, Amazon, Nova, models, automate, operational, readiness, testing, for, new, fulfillment, centers</media:keywords>
</item>

<item>
<title>Iberdrola enhances IT operations using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3931</link>
<guid>https://news.jatlink.uk/3931</guid>
<description><![CDATA[ Iberdrola, one of the world’s largest utility companies, has embraced cutting-edge AI technology to revolutionize its IT operations in ServiceNow. Through its partnership with AWS, Iberdrola implemented different agentic architectures using Amazon Bedrock AgentCore, targeting three key areas: optimizing change request validation in the draft phase, enriching incident management with contextual intelligence, and simplifying change model selection using conversational AI. These innovations reduce bottlenecks, help teams accelerate ticket resolution, and deliver consistent and high-quality data handling throughout the organization. ]]></description>
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<pubDate>Tue, 10 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Iberdrola, enhances, operations, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Building real&amp;time voice assistants with Amazon Nova Sonic compared to cascading architectures</title>
<link>https://news.jatlink.uk/3932</link>
<guid>https://news.jatlink.uk/3932</guid>
<description><![CDATA[ Amazon Nova Sonic delivers real-time, human-like voice conversations through the bidirectional streaming interface. In this post, you learn how Amazon Nova Sonic can solve some of the challenges faced by cascaded approaches, simplify building voice AI agents, and provide natural conversational capabilities. We also provide guidance on when to choose each approach to help you make informed decisions for your voice AI projects. ]]></description>
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<pubDate>Tue, 10 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, real-time, voice, assistants, with, Amazon, Nova, Sonic, compared, cascading, architectures</media:keywords>
</item>

<item>
<title>Automated Reasoning checks rewriting chatbot reference implementation</title>
<link>https://news.jatlink.uk/3858</link>
<guid>https://news.jatlink.uk/3858</guid>
<description><![CDATA[ This blog post dives deeper into the implementation architecture for the Automated Reasoning checks rewriting chatbot. ]]></description>
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<pubDate>Mon, 09 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automated, Reasoning, checks, rewriting, chatbot, reference, implementation</media:keywords>
</item>

<item>
<title>Agent&amp;to&amp;agent collaboration: Using Amazon Nova 2 Lite and Amazon Nova Act for multi&amp;agent systems</title>
<link>https://news.jatlink.uk/3836</link>
<guid>https://news.jatlink.uk/3836</guid>
<description><![CDATA[ This post walks through how agent-to-agent collaboration on Amazon Bedrock works in practice, using Amazon Nova 2 Lite for planning and Amazon Nova Act for browser interaction, to turn a fragile single-agent setup into a predictable multi-agent system. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agent-to-agent, collaboration:, Using, Amazon, Nova, Lite, and, Amazon, Nova, Act, for, multi-agent, systems</media:keywords>
</item>

<item>
<title>Scale LLM fine&amp;tuning with Hugging Face and Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3833</link>
<guid>https://news.jatlink.uk/3833</guid>
<description><![CDATA[ In this post, we show how this integrated approach transforms enterprise LLM fine-tuning from a complex, resource-intensive challenge into a streamlined, scalable solution for achieving better model performance in domain-specific applications. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scale, LLM, fine-tuning, with, Hugging, Face, and, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>New Relic transforms productivity with generative AI on AWS</title>
<link>https://news.jatlink.uk/3834</link>
<guid>https://news.jatlink.uk/3834</guid>
<description><![CDATA[ Working with the Generative AI Innovation Center, New Relic NOVA (New Relic Omnipresence Virtual Assistant) evolved from a knowledge assistant into a comprehensive productivity engine. We explore the technical architecture, development journey, and key lessons learned in building an enterprise-grade AI solution that delivers measurable productivity gains at scale. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>New, Relic, transforms, productivity, with, generative, AWS</media:keywords>
</item>

<item>
<title>Accelerate agentic application development with a full&amp;stack starter template for Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3835</link>
<guid>https://news.jatlink.uk/3835</guid>
<description><![CDATA[ In this post, you will learn how to deploy Fullstack AgentCore Solution Template (FAST) to your Amazon Web Services (AWS) account, understand its architecture, and see how to extend it for your requirements. You will learn how to build your own agent while FAST handles authentication, infrastructure as code (IaC), deployment pipelines, and service integration. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, agentic, application, development, with, full-stack, starter, template, for, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Structured outputs on Amazon Bedrock: Schema&amp;compliant AI responses</title>
<link>https://news.jatlink.uk/3664</link>
<guid>https://news.jatlink.uk/3664</guid>
<description><![CDATA[ Today, we&#039;re announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. In this post, we explore the challenges of traditional JSON generation and how structured outputs solves them. We cover the two core mechanisms—JSON Schema output format and strict tool use—along with implementation details, best practices, and practical code examples. ]]></description>
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<pubDate>Fri, 06 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Structured, outputs, Amazon, Bedrock:, Schema-compliant, responses</media:keywords>
</item>

<item>
<title>Manage Amazon SageMaker HyperPod clusters using the HyperPod CLI and SDK</title>
<link>https://news.jatlink.uk/3663</link>
<guid>https://news.jatlink.uk/3663</guid>
<description><![CDATA[ In this post, we demonstrate how to use the CLI and the SDK to create and manage SageMaker HyperPod clusters in your AWS account. We walk through a practical example and dive deeper into the user workflow and parameter choices. ]]></description>
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<pubDate>Fri, 06 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Manage, Amazon, SageMaker, HyperPod, clusters, using, the, HyperPod, CLI, and, SDK</media:keywords>
</item>

<item>
<title>Evaluate generative AI models with an Amazon Nova rubric&amp;based LLM judge on Amazon SageMaker AI (Part 2)</title>
<link>https://news.jatlink.uk/3641</link>
<guid>https://news.jatlink.uk/3641</guid>
<description><![CDATA[ In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs of two different LLMs using SageMaker training jobs. ]]></description>
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<pubDate>Fri, 06 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluate, generative, models, with, Amazon, Nova, rubric-based, LLM, judge, Amazon, SageMaker, Part</media:keywords>
</item>

<item>
<title>A practical guide to Amazon Nova Multimodal Embeddings</title>
<link>https://news.jatlink.uk/3583</link>
<guid>https://news.jatlink.uk/3583</guid>
<description><![CDATA[ In this post, you will learn how to configure and use Amazon Nova Multimodal Embeddings for media asset search systems, product discovery experiences, and document retrieval applications. ]]></description>
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<pubDate>Thu, 05 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>practical, guide, Amazon, Nova, Multimodal, Embeddings</media:keywords>
</item>

<item>
<title>How Associa transforms document classification with the GenAI IDP Accelerator and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3582</link>
<guid>https://news.jatlink.uk/3582</guid>
<description><![CDATA[ Associa collaborated with the AWS Generative AI Innovation Center to build a generative AI-powered document classification system aligning with Associa’s long-term vision of using generative AI to achieve operational efficiencies in document management. The solution automatically categorizes incoming documents with high accuracy, processes documents efficiently, and provides substantial cost savings while maintaining operational excellence. The document classification system, developed using the Generative AI Intelligent Document Processing (GenAI IDP) Accelerator, is designed to integrate seamlessly into existing workflows. It revolutionizes how employees interact with document management systems by reducing the time spent on manual classification tasks. ]]></description>
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<pubDate>Thu, 05 Feb 2026 21:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Associa, transforms, document, classification, with, the, GenAI, IDP, Accelerator, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Accelerating your marketing ideation with generative AI – Part 2: Generate custom marketing images from historical references</title>
<link>https://news.jatlink.uk/3428</link>
<guid>https://news.jatlink.uk/3428</guid>
<description><![CDATA[ Building upon our earlier work of marketing campaign image generation using Amazon Nova foundation models, in this post, we demonstrate how to enhance image generation by learning from previous marketing campaigns. We explore how to integrate Amazon Bedrock, AWS Lambda, and Amazon OpenSearch Serverless to create an advanced image generation system that uses reference campaigns to maintain brand guidelines, deliver consistent content, and enhance the effectiveness and efficiency of new campaign creation. ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, your, marketing, ideation, with, generative, –, Part, Generate, custom, marketing, images, from, historical, references</media:keywords>
</item>

<item>
<title>Democratizing business intelligence: BGL’s journey with Claude Agent SDK and Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3426</link>
<guid>https://news.jatlink.uk/3426</guid>
<description><![CDATA[ BGL is a leading provider of self-managed superannuation fund (SMSF) administration solutions that help individuals manage the complex compliance and reporting of their own or a client’s retirement savings, serving over 12,700 businesses across 15 countries. In this blog post, we explore how BGL built its production-ready AI agent using Claude Agent SDK and Amazon Bedrock AgentCore. ]]></description>
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<pubDate>Tue, 03 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Democratizing, business, intelligence:, BGL’s, journey, with, Claude, Agent, SDK, and, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Use Amazon Quick Suite custom action connectors to upload text files to Google Drive using OpenAPI specification</title>
<link>https://news.jatlink.uk/3425</link>
<guid>https://news.jatlink.uk/3425</guid>
<description><![CDATA[ In this post, we demonstrate how to build a secure file upload solution by integrating Google Drive with Amazon Quick Suite custom connectors using Amazon API Gateway and AWS Lambda. ]]></description>
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<pubDate>Tue, 03 Feb 2026 20:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Use, Amazon, Quick, Suite, custom, action, connectors, upload, text, files, Google, Drive, using, OpenAPI, specification</media:keywords>
</item>

<item>
<title>AI agents in enterprises: Best practices with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3424</link>
<guid>https://news.jatlink.uk/3424</guid>
<description><![CDATA[ This post explores nine essential best practices for building enterprise AI agents using Amazon Bedrock AgentCore. Amazon Bedrock AgentCore is an agentic platform that provides the services you need to create, deploy, and manage AI agents at scale. In this post, we cover everything from initial scoping to organizational scaling, with practical guidance that you can apply immediately. ]]></description>
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<pubDate>Tue, 03 Feb 2026 19:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>agents, enterprises:, Best, practices, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Agentic AI for healthcare data analysis with Amazon SageMaker Data Agent</title>
<link>https://news.jatlink.uk/3423</link>
<guid>https://news.jatlink.uk/3423</guid>
<description><![CDATA[ On November 21, 2025, Amazon SageMaker introduced a built-in data agent within Amazon SageMaker Unified Studio that transforms large-scale data analysis. In this post, we demonstrate, through a detailed case study of an epidemiologist conducting clinical cohort analysis, how SageMaker Data Agent can help reduce weeks of data preparation into days, and days of analysis development into hours—ultimately accelerating the path from clinical questions to research conclusions. ]]></description>
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<pubDate>Tue, 03 Feb 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, for, healthcare, data, analysis, with, Amazon, SageMaker, Data, Agent</media:keywords>
</item>

<item>
<title>How Clarus Care uses Amazon Bedrock to deliver conversational contact center interactions</title>
<link>https://news.jatlink.uk/3397</link>
<guid>https://news.jatlink.uk/3397</guid>
<description><![CDATA[ In this post, we illustrate how Clarus Care, a healthcare contact center solutions provider, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a generative AI-powered contact center prototype. This solution enables conversational interaction and multi-intent resolution through an automated voicebot and chat interface. It also incorporates a scalable service model to support growth, human transfer capabilities--when requested or for urgent cases--and an analytics pipeline for performance insights. ]]></description>
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<pubDate>Mon, 02 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Clarus, Care, uses, Amazon, Bedrock, deliver, conversational, contact, center, interactions</media:keywords>
</item>

<item>
<title>Evaluating generative AI models with Amazon Nova LLM&amp;as&amp;a&amp;Judge on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3395</link>
<guid>https://news.jatlink.uk/3395</guid>
<description><![CDATA[ Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content generation, […] ]]></description>
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<pubDate>Fri, 30 Jan 2026 22:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, generative, models, with, Amazon, Nova, LLM-as-a-Judge, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Simplify ModelOps with Amazon SageMaker AI Projects using Amazon S3&amp;based templates</title>
<link>https://news.jatlink.uk/3393</link>
<guid>https://news.jatlink.uk/3393</guid>
<description><![CDATA[ This post explores how you can use Amazon S3-based templates to simplify ModelOps workflows, walk through the key benefits compared to using Service Catalog approaches, and demonstrates how to create a custom ModelOps solution that integrates with GitHub and GitHub Actions—giving your team one-click provisioning of a fully functional ML environment. ]]></description>
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<pubDate>Fri, 30 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Simplify, ModelOps, with, Amazon, SageMaker, Projects, using, Amazon, S3-based, templates</media:keywords>
</item>

<item>
<title>Scale AI in South Africa using Amazon Bedrock global cross&amp;Region inference with Anthropic Claude 4.5 models</title>
<link>https://news.jatlink.uk/3394</link>
<guid>https://news.jatlink.uk/3394</guid>
<description><![CDATA[ In this post, we walk through how global cross-Region inference routes requests and where your data resides, then show you how to configure the required AWS Identity and Access Management (IAM) permissions and invoke Claude 4.5 models using the global inference profile Amazon Resource Name (ARN). We also cover how to request quota increases for your workload. By the end, you&#039;ll have a working implementation of global cross-Region inference in af-south-1. ]]></description>
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<pubDate>Fri, 30 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scale, South, Africa, using, Amazon, Bedrock, global, cross-Region, inference, with, Anthropic Claude 4.5, models</media:keywords>
</item>

<item>
<title>Scaling content review operations with multi&amp;agent workflow</title>
<link>https://news.jatlink.uk/3392</link>
<guid>https://news.jatlink.uk/3392</guid>
<description><![CDATA[ The agent-based approach we present is applicable to any type of enterprise content, from product documentation and knowledge bases to marketing materials and technical specifications. To demonstrate these concepts in action, we walk through a practical example of reviewing blog content for technical accuracy. These patterns and techniques can be directly adapted to various content review needs by adjusting the agent configurations, tools, and verification sources. ]]></description>
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<pubDate>Fri, 30 Jan 2026 00:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, content, review, operations, with, multi-agent, workflow</media:keywords>
</item>

<item>
<title>Build reliable Agentic AI solution with Amazon Bedrock: Learn from Pushpay’s journey on GenAI evaluation</title>
<link>https://news.jatlink.uk/3384</link>
<guid>https://news.jatlink.uk/3384</guid>
<description><![CDATA[ In this post, we walk you through Pushpay&#039;s journey in building this solution and explore how Pushpay used Amazon Bedrock to create a custom generative AI evaluation framework for continuous quality assurance and establishing rapid iteration feedback loops on AWS. ]]></description>
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<pubDate>Tue, 27 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, reliable, Agentic, solution, with, Amazon, Bedrock:, Learn, from, Pushpay’s, journey, GenAI, evaluation</media:keywords>
</item>

<item>
<title>Build an intelligent contract management solution with Amazon Quick Suite and Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3382</link>
<guid>https://news.jatlink.uk/3382</guid>
<description><![CDATA[ This blog post demonstrates how to build an intelligent contract management solution using Amazon Quick Suite as your primary contract management solution, augmented with Amazon Bedrock AgentCore for advanced multi-agent capabilities. ]]></description>
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<pubDate>Tue, 27 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, intelligent, contract, management, solution, with, Amazon, Quick, Suite, and, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Build a serverless AI Gateway architecture with AWS AppSync Events</title>
<link>https://news.jatlink.uk/3379</link>
<guid>https://news.jatlink.uk/3379</guid>
<description><![CDATA[ In this post, we discuss how to use AppSync Events as the foundation of a capable, serverless, AI gateway architecture. We explore how it integrates with AWS services for comprehensive coverage of the capabilities offered in AI gateway architectures. Finally, we get you started on your journey with sample code you can launch in your account and begin building. ]]></description>
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<pubDate>Mon, 26 Jan 2026 18:00:39 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, serverless, Gateway, architecture, with, AWS, AppSync, Events</media:keywords>
</item>

<item>
<title>How Totogi automated change request processing with Totogi BSS Magic and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3378</link>
<guid>https://news.jatlink.uk/3378</guid>
<description><![CDATA[ This blog post describes how Totogi automates change request processing by partnering with the AWS Generative AI Innovation Center and using the rapid innovation capabilities of Amazon Bedrock. ]]></description>
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<pubDate>Mon, 26 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Totogi, automated, change, request, processing, with, Totogi, BSS, Magic, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How the Amazon.com Catalog Team built self&amp;learning generative AI at scale with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3375</link>
<guid>https://news.jatlink.uk/3375</guid>
<description><![CDATA[ In this post, we demonstrate how the Amazon Catalog Team built a self-learning system that continuously improves accuracy while reducing costs at scale using Amazon Bedrock. ]]></description>
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<pubDate>Fri, 23 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, the, Amazon.com, Catalog, Team, built, self-learning, generative, scale, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Build AI agents with Amazon Bedrock AgentCore using AWS CloudFormation</title>
<link>https://news.jatlink.uk/3374</link>
<guid>https://news.jatlink.uk/3374</guid>
<description><![CDATA[ Amazon Bedrock AgentCore services are now being supported by various IaC frameworks such as AWS Cloud Development Kit (AWS CDK), Terraform and AWS CloudFormation Templates. This integration brings the power of IaC directly to AgentCore so developers can provision, configure, and manage their AI agent infrastructure. In this post, we use CloudFormation templates to build an end-to-end application for a weather activity planner. ]]></description>
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<pubDate>Fri, 23 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, agents, with, Amazon, Bedrock, AgentCore, using, AWS, CloudFormation</media:keywords>
</item>

<item>
<title>How CLICKFORCE accelerates data&amp;driven advertising with Amazon Bedrock Agents</title>
<link>https://news.jatlink.uk/3372</link>
<guid>https://news.jatlink.uk/3372</guid>
<description><![CDATA[ In this post, we demonstrate how CLICKFORCE used AWS services to build Lumos and transform advertising industry analysis from weeks-long manual work into an automated, one-hour process. ]]></description>
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<pubDate>Thu, 22 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, CLICKFORCE, accelerates, data-driven, advertising, with, Amazon, Bedrock, Agents</media:keywords>
</item>

<item>
<title>How PDI built an enterprise&amp;grade RAG system for AI applications with AWS</title>
<link>https://news.jatlink.uk/3371</link>
<guid>https://news.jatlink.uk/3371</guid>
<description><![CDATA[ PDI Technologies is a global leader in the convenience retail and petroleum wholesale industries. In this post, we walk through the PDI Intelligence Query (PDIQ) process flow and architecture, focusing on the implementation details and the business outcomes it has helped PDI achieve. ]]></description>
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<pubDate>Thu, 22 Jan 2026 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, PDI, built, enterprise-grade, RAG, system, for, applications, with, AWS</media:keywords>
</item>

<item>
<title>How Thomson Reuters built an Agentic Platform Engineering Hub with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3369</link>
<guid>https://news.jatlink.uk/3369</guid>
<description><![CDATA[ This blog post explains how TR&#039;s Platform Engineering team, a geographically distributed unit overseeing TR&#039;s service availability, boosted its operational productivity by transitioning from manual to an automated agentic system using Amazon Bedrock AgentCore. ]]></description>
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<pubDate>Wed, 21 Jan 2026 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Thomson, Reuters, built, Agentic, Platform, Engineering, Hub, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Build agents to learn from experiences using Amazon Bedrock AgentCore episodic memory</title>
<link>https://news.jatlink.uk/3368</link>
<guid>https://news.jatlink.uk/3368</guid>
<description><![CDATA[ In this post, we walk you through the complete architecture to structure and store episodes, discuss the reflection module, and share compelling benchmarks that demonstrate significant improvements in agent task success rates. ]]></description>
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<pubDate>Wed, 21 Jan 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, agents, learn, from, experiences, using, Amazon, Bedrock, AgentCore, episodic, memory</media:keywords>
</item>

<item>
<title>How bunq handles 97% of support with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3365</link>
<guid>https://news.jatlink.uk/3365</guid>
<description><![CDATA[ In this post, we show how bunq upgraded Finn, its in-house generative AI assistant, using Amazon Bedrock to transform user support and banking operations to be seamless, in multiple languages and time zones. ]]></description>
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<pubDate>Wed, 21 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, bunq, handles, 97, support, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Using Strands Agents to create a multi&amp;agent solution with Meta’s Llama 4 and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3366</link>
<guid>https://news.jatlink.uk/3366</guid>
<description><![CDATA[ In this post, we explore how to build a multi-agent video processing workflow using Strands Agents, Meta&#039;s Llama 4 models, and Amazon Bedrock to automatically analyze and understand video content through specialized AI agents working in coordination. To showcase the solution, we will use Amazon SageMaker AI to walk you through the code. ]]></description>
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<pubDate>Wed, 21 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Using, Strands, Agents, create, multi-agent, solution, with, Meta’s, Llama, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Introducing multimodal retrieval for Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/3322</link>
<guid>https://news.jatlink.uk/3322</guid>
<description><![CDATA[ In this post, we&#039;ll guide you through building multimodal RAG applications. You&#039;ll learn how multimodal knowledge bases work, how to choose the right processing strategy based on your content type, and how to configure and implement multimodal retrieval using both the console and code examples. ]]></description>
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<pubDate>Tue, 20 Jan 2026 19:00:11 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, multimodal, retrieval, for, Amazon, Bedrock, Knowledge, Bases</media:keywords>
</item>

<item>
<title>Deploy AI agents on Amazon Bedrock AgentCore using GitHub Actions</title>
<link>https://news.jatlink.uk/3314</link>
<guid>https://news.jatlink.uk/3314</guid>
<description><![CDATA[ In this post, we demonstrate how to use a GitHub Actions workflow to automate the deployment of AI agents on AgentCore Runtime. This approach delivers a scalable solution with enterprise-level security controls, providing complete continuous integration and delivery (CI/CD) automation. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, agents, Amazon, Bedrock, AgentCore, using, GitHub, Actions</media:keywords>
</item>

<item>
<title>How Palo Alto Networks enhanced device security infra log analysis with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3312</link>
<guid>https://news.jatlink.uk/3312</guid>
<description><![CDATA[ Palo Alto Networks’ Device Security team wanted to detect early warning signs of potential production issues to provide more time to SMEs to react to these emerging problems. They partnered with the AWS Generative AI Innovation Center (GenAIIC) to develop an automated log classification pipeline powered by Amazon Bedrock. In this post, we discuss how Amazon Bedrock, through Anthropic’ s Claude Haiku model, and Amazon Titan Text Embeddings work together to automatically classify and analyze log data. We explore how this automated pipeline detects critical issues, examine the solution architecture, and share implementation insights that have delivered measurable operational improvements. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Palo, Alto, Networks, enhanced, device, security, infra, log, analysis, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>From beginner to champion: A student’s journey through the AWS AI League ASEAN finals</title>
<link>https://news.jatlink.uk/3313</link>
<guid>https://news.jatlink.uk/3313</guid>
<description><![CDATA[ The AWS AI League, launched by Amazon Web Services (AWS), expanded its reach to the Association of Southeast Asian Nations (ASEAN) last year, welcoming student participants from Singapore, Indonesia, Malaysia, Thailand, Vietnam, and the Philippines. In this blog post, you’ll hear directly from the AWS AI League champion, Blix D. Foryasen, as he shares his reflection on the challenges, breakthroughs, and key lessons discovered throughout the competition. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, beginner, champion:, student’s, journey, through, the, AWS, League, ASEAN, finals</media:keywords>
</item>

<item>
<title>Advanced fine&amp;tuning techniques for multi&amp;agent orchestration: Patterns from Amazon at scale</title>
<link>https://news.jatlink.uk/3311</link>
<guid>https://news.jatlink.uk/3311</guid>
<description><![CDATA[ In this post, we show you how fine-tuning enabled a 33% reduction in dangerous medication errors (Amazon Pharmacy), engineering 80% human effort reduction (Amazon Global Engineering Services), and content quality assessments improving 77% to 96% accuracy (Amazon A+). This post details the techniques behind these outcomes: from foundational methods like Supervised Fine-Tuning (SFT) (instruction tuning), and Proximal Policy Optimization (PPO), to Direct Preference Optimization (DPO) for human alignment, to cutting-edge reasoning optimizations such as Grouped-based Reinforcement Learning from Policy Optimization (GRPO), Direct Advantage Policy Optimization (DAPO), and Group Sequence Policy Optimization (GSPO) purpose-built for agentic systems. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advanced, fine-tuning, techniques, for, multi-agent, orchestration:, Patterns, from, Amazon, scale</media:keywords>
</item>

<item>
<title>Safeguard generative AI applications with Amazon Bedrock Guardrails</title>
<link>https://news.jatlink.uk/3308</link>
<guid>https://news.jatlink.uk/3308</guid>
<description><![CDATA[ In this post, we demonstrate how you can address these challenges by adding centralized safeguards to a custom multi-provider generative AI gateway using Amazon Bedrock Guardrails. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Safeguard, generative, applications, with, Amazon, Bedrock, Guardrails</media:keywords>
</item>

<item>
<title>Scale creative asset discovery with Amazon Nova Multimodal Embeddings unified vector search</title>
<link>https://news.jatlink.uk/3309</link>
<guid>https://news.jatlink.uk/3309</guid>
<description><![CDATA[ In this post, we describe how you can use Amazon Nova Multimodal Embeddings to retrieve specific video segments. We also review a real-world use case in which Nova Multimodal Embeddings achieved a recall success rate of 96.7% and a high-precision recall of 73.3% (returning the target content in the top two results) when tested against a library of 170 gaming creative assets. The model also demonstrates strong cross-language capabilities with minimal performance degradation across multiple languages. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scale, creative, asset, discovery, with, Amazon, Nova, Multimodal, Embeddings, unified, vector, search</media:keywords>
</item>

<item>
<title>Build a generative AI&amp;powered business reporting solution with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3307</link>
<guid>https://news.jatlink.uk/3307</guid>
<description><![CDATA[ This post introduces generative AI guided business reporting—with a focus on writing achievements &amp; challenges about your business—providing a smart, practical solution that helps simplify and accelerate internal communication and reporting. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, generative, AI-powered, business, reporting, solution, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How the Amazon AMET Payments team accelerates test case generation with Strands Agents</title>
<link>https://news.jatlink.uk/3306</link>
<guid>https://news.jatlink.uk/3306</guid>
<description><![CDATA[ In this post, we explain how we overcame the limitations of single-agent AI systems through a human-centric approach, implemented structured outputs to significantly reduce hallucinations and built a scalable solution now positioned for expansion across the AMET QA team and later across other QA teams in International Emerging Stores and Payments (IESP) Org. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, the, Amazon, AMET, Payments, team, accelerates, test, case, generation, with, Strands, Agents</media:keywords>
</item>

<item>
<title>Transform AI development with new Amazon SageMaker AI model customization and large&amp;scale training capabilities</title>
<link>https://news.jatlink.uk/3304</link>
<guid>https://news.jatlink.uk/3304</guid>
<description><![CDATA[ This post explores how new serverless model customization capabilities, elastic training, checkpointless training, and serverless MLflow work together to accelerate your AI development from months to days. ]]></description>
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<pubDate>Wed, 14 Jan 2026 22:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Transform, development, with, new, Amazon, SageMaker, model, customization, and, large-scale, training, capabilities</media:keywords>
</item>

<item>
<title>How AutoScout24 built a Bot Factory to standardize AI agent development with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3303</link>
<guid>https://news.jatlink.uk/3303</guid>
<description><![CDATA[ In this post, we explore the architecture that AutoScout24 used to build their standardized AI development framework, enabling rapid deployment of secure and scalable AI agents. ]]></description>
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<pubDate>Wed, 14 Jan 2026 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, AutoScout24, built, Bot, Factory, standardize, agent, development, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Securing Amazon Bedrock cross&amp;Region inference: Geographic and global</title>
<link>https://news.jatlink.uk/3300</link>
<guid>https://news.jatlink.uk/3300</guid>
<description><![CDATA[ In this post, we explore the security considerations and best practices for implementing Amazon Bedrock cross-Region inference profiles. Whether you&#039;re building a generative AI application or need to meet specific regional compliance requirements, this guide will help you understand the secure architecture of Amazon Bedrock CRIS and how to properly configure your implementation. ]]></description>
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<pubDate>Wed, 14 Jan 2026 00:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Securing, Amazon, Bedrock, cross-Region, inference:, Geographic, and, global</media:keywords>
</item>

<item>
<title>How Beekeeper by LumApps optimized user personalization with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3299</link>
<guid>https://news.jatlink.uk/3299</guid>
<description><![CDATA[ Beekeeper’s automated leaderboard approach and human feedback loop system for dynamic LLM and prompt pair selection addresses the key challenges organizations face in navigating the rapidly evolving landscape of language models. ]]></description>
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<pubDate>Mon, 12 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Beekeeper, LumApps, optimized, user, personalization, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Omada Health scaled patient care by fine&amp;tuning Llama models on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3298</link>
<guid>https://news.jatlink.uk/3298</guid>
<description><![CDATA[ This post is co-written with Sunaina Kavi, AI/ML Product Manager at Omada Health. Omada Health, a longtime innovator in virtual healthcare delivery, launched a new nutrition experience in 2025, featuring OmadaSpark, an AI agent trained with robust clinical input that delivers real-time motivational interviewing and nutrition education. It was built on AWS. OmadaSpark was designed […] ]]></description>
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<pubDate>Mon, 12 Jan 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Omada, Health, scaled, patient, care, fine-tuning, Llama, models, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Crossmodal search with Amazon Nova Multimodal Embeddings</title>
<link>https://news.jatlink.uk/3297</link>
<guid>https://news.jatlink.uk/3297</guid>
<description><![CDATA[ In this post, we explore how Amazon Nova Multimodal Embeddings addresses the challenges of crossmodal search through a practical ecommerce use case. We examine the technical limitations of traditional approaches and demonstrate how Amazon Nova Multimodal Embeddings enables retrieval across text, images, and other modalities. You learn how to implement a crossmodal search system by generating embeddings, handling queries, and measuring performance. We provide working code examples and share how to add these capabilities to your applications. ]]></description>
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<pubDate>Sat, 10 Jan 2026 01:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Crossmodal, search, with, Amazon, Nova, Multimodal, Embeddings</media:keywords>
</item>

<item>
<title>Accelerating LLM inference with post&amp;training weight and activation using AWQ and GPTQ on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3295</link>
<guid>https://news.jatlink.uk/3295</guid>
<description><![CDATA[ Quantized models can be seamlessly deployed on Amazon SageMaker AI using a few lines of code. In this post, we explore why quantization matters—how it enables lower-cost inference, supports deployment on resource-constrained hardware, and reduces both the financial and environmental impact of modern LLMs, while preserving most of their original performance. We also take a deep dive into the principles behind PTQ and demonstrate how to quantize the model of your choice and deploy it on Amazon SageMaker. ]]></description>
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<pubDate>Fri, 09 Jan 2026 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, LLM, inference, with, post-training, weight, and, activation, using, AWQ, and, GPTQ, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Sentiment Analysis with Text and Audio Using AWS Generative AI Services: Approaches, Challenges, and Solutions</title>
<link>https://news.jatlink.uk/3293</link>
<guid>https://news.jatlink.uk/3293</guid>
<description><![CDATA[ This post, developed through a strategic scientific partnership between AWS and the Instituto de Ciência e Tecnologia Itaú (ICTi), P&amp;D hub maintained by Itaú Unibanco, the largest private bank in Latin America, explores the technical aspects of sentiment analysis for both text and audio. We present experiments comparing multiple machine learning (ML) models and services, discuss the trade-offs and pitfalls of each approach, and highlight how AWS services can be orchestrated to build robust, end-to-end solutions. We also offer insights into potential future directions, including more advanced prompt engineering for large language models (LLMs) and expanding the scope of audio-based analysis to capture emotional cues that text data alone might miss. ]]></description>
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<pubDate>Fri, 09 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Sentiment, Analysis, with, Text, and, Audio, Using, AWS, Generative, Services:, Approaches, Challenges, and, Solutions</media:keywords>
</item>

<item>
<title>Architecting TrueLook’s AI&amp;powered construction safety system on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3294</link>
<guid>https://news.jatlink.uk/3294</guid>
<description><![CDATA[ This post provides a detailed architectural overview of how TrueLook built its AI-powered safety monitoring system using SageMaker AI, highlighting key technical decisions, pipeline design patterns, and MLOps best practices. You will gain valuable insights into designing scalable computer vision solutions on AWS, particularly around model training workflows, automated pipeline creation, and production deployment strategies for real-time inference. ]]></description>
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<pubDate>Fri, 09 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Architecting, TrueLook’s, AI-powered, construction, safety, system, Amazon, SageMaker AI</media:keywords>
</item>

<item>
<title>How Beekeeper optimized user personalization with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3292</link>
<guid>https://news.jatlink.uk/3292</guid>
<description><![CDATA[ Beekeeper’s automated leaderboard approach and human feedback loop system for dynamic LLM and prompt pair selection addresses the key challenges organizations face in navigating the rapidly evolving landscape of language models. ]]></description>
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<pubDate>Fri, 09 Jan 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Beekeeper, optimized, user, personalization, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Scaling medical content review at Flo Health using Amazon Bedrock (Part 1)</title>
<link>https://news.jatlink.uk/3288</link>
<guid>https://news.jatlink.uk/3288</guid>
<description><![CDATA[ This two-part series explores Flo Health&#039;s journey with generative AI for medical content verification. Part 1 examines our proof of concept (PoC), including the initial solution, capabilities, and early results. Part 2 covers focusing on scaling challenges and real-world implementation. Each article stands alone while collectively showing how AI transforms medical content management at scale. ]]></description>
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<pubDate>Thu, 08 Jan 2026 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, medical, content, review, Flo, Health, using, Amazon, Bedrock, Part</media:keywords>
</item>

<item>
<title>Detect and redact personally identifiable information using Amazon Bedrock Data Automation and Guardrails</title>
<link>https://news.jatlink.uk/3286</link>
<guid>https://news.jatlink.uk/3286</guid>
<description><![CDATA[ This post shows an automated PII detection and redaction solution using Amazon Bedrock Data Automation and Amazon Bedrock Guardrails through a use case of processing text and image content in high volumes of incoming emails and attachments. The solution features a complete email processing workflow with a React-based user interface for authorized personnel to more securely manage and review redacted email communications and attachments. We walk through the step-by-step solution implementation procedures used to deploy this solution. Finally, we discuss the solution benefits, including operational efficiency, scalability, security and compliance, and adaptability. ]]></description>
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<pubDate>Thu, 08 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Detect, and, redact, personally, identifiable, information, using, Amazon, Bedrock, Data, Automation, and, Guardrails</media:keywords>
</item>

<item>
<title>Speed meets scale: Load testing SageMakerAI endpoints with Observe.AI’s testing tool</title>
<link>https://news.jatlink.uk/3287</link>
<guid>https://news.jatlink.uk/3287</guid>
<description><![CDATA[ Observe.ai developed the One Load Audit Framework (OLAF), which integrates with SageMaker to identify bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data loads. In this blog post, you will learn how to use the OLAF utility to test and validate your SageMaker endpoint. ]]></description>
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<pubDate>Thu, 08 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Speed, meets, scale:, Load, testing, SageMakerAI, endpoints, with, Observe.AI’s, testing, tool</media:keywords>
</item>

<item>
<title>Migrate MLflow tracking servers to Amazon SageMaker AI with serverless MLflow</title>
<link>https://news.jatlink.uk/3282</link>
<guid>https://news.jatlink.uk/3282</guid>
<description><![CDATA[ This post shows you how to migrate your self-managed MLflow tracking server to a MLflow App – a serverless tracking server on SageMaker AI that automatically scales resources based on demand while removing server patching and storage management tasks at no cost. Learn how to use the MLflow Export Import tool to transfer your experiments, runs, models, and other MLflow resources, including instructions to validate your migration&#039;s success. ]]></description>
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<pubDate>Mon, 29 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Migrate, MLflow, tracking, servers, Amazon, SageMaker, with, serverless, MLflow</media:keywords>
</item>

<item>
<title>Build an AI&amp;powered website assistant with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3281</link>
<guid>https://news.jatlink.uk/3281</guid>
<description><![CDATA[ This post demonstrates how to solve this challenge by building an AI-powered website assistant using Amazon Bedrock and Amazon Bedrock Knowledge Bases. ]]></description>
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<pubDate>Mon, 29 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-powered, website, assistant, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Agentic QA automation using Amazon Bedrock AgentCore Browser and Amazon Nova Act</title>
<link>https://news.jatlink.uk/3279</link>
<guid>https://news.jatlink.uk/3279</guid>
<description><![CDATA[ In this post, we explore how agentic QA automation addresses these challenges and walk through a practical example using Amazon Bedrock AgentCore Browser and Amazon Nova Act to automate testing for a sample retail application. ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, automation, using, Amazon, Bedrock, AgentCore, Browser, and, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Optimizing LLM inference on Amazon SageMaker AI with BentoML’s LLM&amp; Optimizer</title>
<link>https://news.jatlink.uk/3280</link>
<guid>https://news.jatlink.uk/3280</guid>
<description><![CDATA[ In this post, we demonstrate how to optimize large language model (LLM) inference on Amazon SageMaker AI using BentoML&#039;s LLM-Optimizer to systematically identify the best serving configurations for your workload. ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Optimizing, LLM, inference, Amazon, SageMaker, with, BentoML’s, LLM-, Optimizer</media:keywords>
</item>

<item>
<title>AI agent&amp;driven browser automation for enterprise workflow management</title>
<link>https://news.jatlink.uk/3278</link>
<guid>https://news.jatlink.uk/3278</guid>
<description><![CDATA[ Enterprise organizations increasingly rely on web-based applications for critical business processes, yet many workflows remain manually intensive, creating operational inefficiencies and compliance risks. Despite significant technology investments, knowledge workers routinely navigate between eight to twelve different web applications during standard workflows, constantly switching contexts and manually transferring information between systems. Data entry and validation tasks […] ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>agent-driven, browser, automation, for, enterprise, workflow, management</media:keywords>
</item>

<item>
<title>Programmatically creating an IDP solution with Amazon Bedrock Data Automation</title>
<link>https://news.jatlink.uk/3277</link>
<guid>https://news.jatlink.uk/3277</guid>
<description><![CDATA[ In this post, we explore how to programmatically create an IDP solution that uses Strands SDK, Amazon Bedrock AgentCore, Amazon Bedrock Knowledge Base, and Bedrock Data Automation (BDA). This solution is provided through a Jupyter notebook that enables users to upload multi-modal business documents and extract insights using BDA as a parser to retrieve relevant chunks and augment a prompt to a foundational model (FM). ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Programmatically, creating, IDP, solution, with, Amazon, Bedrock, Data, Automation</media:keywords>
</item>

<item>
<title>Exploring the zero operator access design of Mantle</title>
<link>https://news.jatlink.uk/3276</link>
<guid>https://news.jatlink.uk/3276</guid>
<description><![CDATA[ In this post, we explore how Mantle, Amazon&#039;s next-generation inference engine for Amazon Bedrock, implements a zero operator access (ZOA) design that eliminates any technical means for AWS operators to access customer data. ]]></description>
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<pubDate>Tue, 23 Dec 2025 23:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Exploring, the, zero, operator, access, design, Mantle</media:keywords>
</item>

<item>
<title>How dLocal automated compliance reviews using Amazon Quick Automate</title>
<link>https://news.jatlink.uk/3273</link>
<guid>https://news.jatlink.uk/3273</guid>
<description><![CDATA[ In this post, we share how dLocal worked closely with the AWS team to help shape the product roadmap, reinforce its role as an industry innovator, and set new benchmarks for operational excellence in the global fintech landscape. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, dLocal, automated, compliance, reviews, using, Amazon, Quick, Automate</media:keywords>
</item>

<item>
<title>Advancing ADHD diagnosis: How Qbtech built a mobile AI assessment Model Using Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3274</link>
<guid>https://news.jatlink.uk/3274</guid>
<description><![CDATA[ In this post, we explore how Qbtech streamlined their machine learning (ML) workflow using Amazon SageMaker AI, a fully managed service to build, train and deploy ML models, and AWS Glue, a serverless service that makes data integration simpler, faster, and more cost effective. This new solution reduced their feature engineering time from weeks to hours, while maintaining the high clinical standards required by healthcare providers. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advancing, ADHD, diagnosis:, How, Qbtech, built, mobile, assessment, Model, Using, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Accelerating your marketing ideation with generative AI – Part 1: From idea to generation with the Amazon Nova foundation models</title>
<link>https://news.jatlink.uk/3275</link>
<guid>https://news.jatlink.uk/3275</guid>
<description><![CDATA[ In this post, the first of a series of three, we focus on how you can use Amazon Nova to streamline, simplify, and accelerate marketing campaign creation through generative AI. We show how Bancolombia, one of Colombia’s largest banks, is experimenting with the Amazon Nova models to generate visuals for their marketing campaigns. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, your, marketing, ideation, with, generative, –, Part, From, idea, generation, with, the, Amazon, Nova, foundation, models</media:keywords>
</item>

<item>
<title>AWS AI League: Model customization and agentic showdown</title>
<link>https://news.jatlink.uk/3271</link>
<guid>https://news.jatlink.uk/3271</guid>
<description><![CDATA[ In this post, we explore the new AWS AI League challenges and how they are transforming how organizations approach AI development. The grand finale at AWS re:Invent 2025 was an exciting showcase of their ingenuity and skills. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, League:, Model, customization, and, agentic, showdown</media:keywords>
</item>

<item>
<title>Accelerate Enterprise AI Development using Weights &amp;amp; Biases and Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3272</link>
<guid>https://news.jatlink.uk/3272</guid>
<description><![CDATA[ In this post, we demonstrate how to use Foundation Models (FMs) from Amazon Bedrock and the newly launched Amazon Bedrock AgentCore alongside W&amp;B Weave to help build, evaluate, and monitor enterprise AI solutions. We cover the complete development lifecycle from tracking individual FM calls to monitoring complex agent workflows in production. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, Enterprise, Development, using, Weights, Biases, and, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Introducing Visa Intelligent Commerce on AWS: Enabling agentic commerce with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3270</link>
<guid>https://news.jatlink.uk/3270</guid>
<description><![CDATA[ In this post, we explore how AWS and Visa are partnering to enable agentic commerce through Visa Intelligent Commerce using Amazon Bedrock AgentCore. We demonstrate how autonomous AI agents can transform fragmented shopping and travel experiences into seamless, end-to-end workflows—from discovery and comparison to secure payment authorization—all driven by natural language. ]]></description>
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<pubDate>Tue, 23 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Visa, Intelligent, Commerce, AWS:, Enabling, agentic, commerce, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Deploy Mistral AI’s Voxtral on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3267</link>
<guid>https://news.jatlink.uk/3267</guid>
<description><![CDATA[ In this post, we demonstrate hosting Voxtral models on Amazon SageMaker AI endpoints using vLLM and the Bring Your Own Container (BYOC) approach. vLLM is a high-performance library for serving large language models (LLMs) that features paged attention for improved memory management and tensor parallelism for distributing models across multiple GPUs. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, Mistral, AI’s, Voxtral, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Enhance document analytics with Strands AI Agents for the GenAI IDP Accelerator</title>
<link>https://news.jatlink.uk/3268</link>
<guid>https://news.jatlink.uk/3268</guid>
<description><![CDATA[ To address the need for businesses to quickly analyze information and unlock actionable insights, we are announcing Analytics Agent, a new feature that is seamlessly integrated into the GenAI IDP Accelerator. With this feature, users can perform advanced searches and complex analyses using natural language queries without SQL or data analysis expertise. In this post, we discuss how non-technical users can use this tool to analyze and understand the documents they have processed at scale with natural language. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enhance, document, analytics, with, Strands, Agents, for, the, GenAI, IDP, Accelerator</media:keywords>
</item>

<item>
<title>Build a multimodal generative AI assistant for root cause diagnosis in predictive maintenance using Amazon Bedrock</title>
<link>https://news.jatlink.uk/3269</link>
<guid>https://news.jatlink.uk/3269</guid>
<description><![CDATA[ In this post, we demonstrate how to implement a predictive maintenance solution using Foundation Models (FMs) on Amazon Bedrock, with a case study of Amazon&#039;s manufacturing equipment within their fulfillment centers. The solution is highly adaptable and can be customized for other industries, including oil and gas, logistics, manufacturing, and healthcare. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, multimodal, generative, assistant, for, root, cause, diagnosis, predictive, maintenance, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Move Beyond Chain&amp;of&amp;Thought with Chain&amp;of&amp;Draft on Amazon Bedrock</title>
<link>https://news.jatlink.uk/3266</link>
<guid>https://news.jatlink.uk/3266</guid>
<description><![CDATA[ This post explores Chain-of-Draft (CoD), an innovative prompting technique introduced in a Zoom AI Research paper Chain of Draft: Thinking Faster by Writing Less, that revolutionizes how models approach reasoning tasks. While Chain-of-Thought (CoT) prompting has been the go-to method for enhancing model reasoning, CoD offers a more efficient alternative that mirrors human problem-solving patterns—using concise, high-signal thinking steps rather than verbose explanations. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Move, Beyond, Chain-of-Thought, with, Chain-of-Draft, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Introducing SOCI indexing for Amazon SageMaker Studio: Faster container startup times for AI/ML workloads</title>
<link>https://news.jatlink.uk/3263</link>
<guid>https://news.jatlink.uk/3263</guid>
<description><![CDATA[ Today, we are excited to introduce a new feature for SageMaker Studio: SOCI (Seekable Open Container Initiative) indexing. SOCI supports lazy loading of container images, where only the necessary parts of an image are downloaded initially rather than the entire container. ]]></description>
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<pubDate>Fri, 19 Dec 2025 19:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, SOCI, indexing, for, Amazon, SageMaker, Studio:, Faster, container, startup, times, for, AIML, workloads</media:keywords>
</item>

<item>
<title>Build and deploy scalable AI agents with NVIDIA NeMo, Amazon Bedrock AgentCore, and Strands Agents</title>
<link>https://news.jatlink.uk/3255</link>
<guid>https://news.jatlink.uk/3255</guid>
<description><![CDATA[ This post demonstrates how to use the powerful combination of Strands Agents, Amazon Bedrock AgentCore, and NVIDIA NeMo Agent Toolkit to build, evaluate, optimize, and deploy AI agents on Amazon Web Services (AWS) from initial development through production deployment. ]]></description>
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<pubDate>Thu, 18 Dec 2025 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, and, deploy, scalable, agents, with, NVIDIA, NeMo, Amazon, Bedrock, AgentCore, and, Strands, Agents</media:keywords>
</item>

<item>
<title>Bi&amp;directional streaming for real&amp;time agent interactions now available in Amazon Bedrock AgentCore Runtime</title>
<link>https://news.jatlink.uk/3256</link>
<guid>https://news.jatlink.uk/3256</guid>
<description><![CDATA[ In this post, you will learn about bi-directional streaming on AgentCore Runtime and the prerequisites to create a WebSocket implementation. You will also learn how to use Strands Agents to implement a bi-directional streaming solution for voice agents. ]]></description>
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<pubDate>Thu, 18 Dec 2025 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Bi-directional, streaming, for, real-time, agent, interactions, now, available, Amazon, Bedrock, AgentCore, Runtime</media:keywords>
</item>

<item>
<title>Track machine learning experiments with MLflow on Amazon SageMaker using Snowflake integration</title>
<link>https://news.jatlink.uk/3253</link>
<guid>https://news.jatlink.uk/3253</guid>
<description><![CDATA[ In this post, we demonstrate how to integrate Amazon SageMaker managed MLflow as a central repository to log these experiments and provide a unified system for monitoring their progress. ]]></description>
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<pubDate>Wed, 17 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Track, machine, learning, experiments, with, MLflow, Amazon, SageMaker, using, Snowflake, integration</media:keywords>
</item>

<item>
<title>Tracking and managing assets used in AI development with Amazon SageMaker AI </title>
<link>https://news.jatlink.uk/3252</link>
<guid>https://news.jatlink.uk/3252</guid>
<description><![CDATA[ In this post, we&#039;ll explore the new capabilities and core concepts that help organizations track and manage models development and deployment lifecycles. We will show you how the features are configured to train models with automatic end-to-end lineage, from dataset upload and versioning to model fine-tuning, evaluation, and seamless endpoint deployment. ]]></description>
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<pubDate>Wed, 17 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Tracking, and, managing, assets, used, development, with Amazon, SageMaker, AI </media:keywords>
</item>

<item>
<title>Governance by design: The essential guide for successful AI scaling</title>
<link>https://news.jatlink.uk/3250</link>
<guid>https://news.jatlink.uk/3250</guid>
<description><![CDATA[ Picture this: Your enterprise has just deployed its first generative AI application. The initial results are promising, but as you plan to scale across departments, critical questions emerge. How will you enforce consistent security, prevent model bias, and maintain control as AI applications multiply? ]]></description>
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<pubDate>Tue, 16 Dec 2025 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Governance, design:, The, essential, guide, for, successful, scaling</media:keywords>
</item>

<item>
<title>How Tata Power CoE built a scalable AI&amp;powered solar panel inspection solution with Amazon SageMaker AI and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3248</link>
<guid>https://news.jatlink.uk/3248</guid>
<description><![CDATA[ In this post, we explore how Tata Power CoE and Oneture Technologies use AWS services to automate the inspection process end-to-end. ]]></description>
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<pubDate>Tue, 16 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Tata, Power, CoE, built, scalable, AI-powered, solar, panel, inspection, solution, with, Amazon, SageMaker, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Unlocking video understanding with TwelveLabs Marengo on Amazon Bedrock</title>
<link>https://news.jatlink.uk/3249</link>
<guid>https://news.jatlink.uk/3249</guid>
<description><![CDATA[ In this post, we&#039;ll show how the TwelveLabs Marengo embedding model, available on Amazon Bedrock, enhances video understanding through multimodal AI. We&#039;ll build a video semantic search and analysis solution using embeddings from the Marengo model with Amazon OpenSearch Serverless as the vector database, for semantic search capabilities that go beyond simple metadata matching to deliver intelligent content discovery. ]]></description>
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<pubDate>Tue, 16 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unlocking, video, understanding, with, TwelveLabs, Marengo, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Checkpointless training on Amazon SageMaker HyperPod: Production&amp;scale training with faster fault recovery</title>
<link>https://news.jatlink.uk/3244</link>
<guid>https://news.jatlink.uk/3244</guid>
<description><![CDATA[ In this post, we introduce checkpointless training on Amazon SageMaker HyperPod, a paradigm shift in model training that reduces the need for traditional checkpointing by enabling peer-to-peer state recovery. Results from production-scale validation show 80–93% reduction in recovery time (from 15–30 minutes or more to under 2 minutes) and enables up to 95% training goodput on cluster sizes with thousands of AI accelerators. ]]></description>
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<pubDate>Mon, 15 Dec 2025 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Checkpointless, training, Amazon, SageMaker, HyperPod:, Production-scale, training, with, faster, fault, recovery</media:keywords>
</item>

<item>
<title>Adaptive infrastructure for foundation model training with elastic training on SageMaker HyperPod</title>
<link>https://news.jatlink.uk/3243</link>
<guid>https://news.jatlink.uk/3243</guid>
<description><![CDATA[ Amazon SageMaker HyperPod now supports elastic training, enabling your machine learning (ML) workloads to automatically scale based on resource availability. In this post, we demonstrate how elastic training helps you maximize GPU utilization, reduce costs, and accelerate model development through dynamic resource adaptation, while maintain training quality and minimizing manual intervention. ]]></description>
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<pubDate>Mon, 15 Dec 2025 19:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Adaptive, infrastructure, for, foundation, model, training, with, elastic, training, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>Applying data loading best practices for ML training with Amazon S3 clients</title>
<link>https://news.jatlink.uk/3242</link>
<guid>https://news.jatlink.uk/3242</guid>
<description><![CDATA[ In this post, we present practical techniques and recommendations for optimizing throughput in ML training workloads that read data directly from Amazon S3 general purpose buckets. ]]></description>
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<pubDate>Mon, 15 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Applying, data, loading, best, practices, for, training, with, Amazon, clients</media:keywords>
</item>

<item>
<title>Customize agent workflows with advanced orchestration techniques using Strands Agents</title>
<link>https://news.jatlink.uk/3240</link>
<guid>https://news.jatlink.uk/3240</guid>
<description><![CDATA[ In this post, we explore two powerful orchestration patterns implemented with Strands Agents. Using a common set of travel planning tools, we demonstrate how different orchestration strategies can solve the same problem through distinct reasoning approaches, ]]></description>
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<pubDate>Mon, 15 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Customize, agent, workflows, with, advanced, orchestration, techniques, using, Strands, Agents</media:keywords>
</item>

<item>
<title>Operationalize generative AI workloads and scale to hundreds of use cases with Amazon Bedrock – Part 1: GenAIOps</title>
<link>https://news.jatlink.uk/3241</link>
<guid>https://news.jatlink.uk/3241</guid>
<description><![CDATA[ In this first part of our two-part series, you&#039;ll learn how to evolve your existing DevOps architecture for generative AI workloads and implement GenAIOps practices. We&#039;ll showcase practical implementation strategies for different generative AI adoption levels, focusing on consuming foundation models. ]]></description>
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<pubDate>Mon, 15 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Operationalize, generative, workloads, and, scale, hundreds, use, cases, with, Amazon, Bedrock, –, Part, GenAIOps</media:keywords>
</item>

<item>
<title>Building a voice&amp;driven AWS assistant with Amazon Nova Sonic</title>
<link>https://news.jatlink.uk/3239</link>
<guid>https://news.jatlink.uk/3239</guid>
<description><![CDATA[ In this post, we explore how to build a sophisticated voice-powered AWS operations assistant using Amazon Nova Sonic for speech processing and Strands Agents for multi-agent orchestration. This solution demonstrates how natural language voice interactions can transform cloud operations, making AWS services more accessible and operations more efficient. ]]></description>
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<pubDate>Fri, 12 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, voice-driven, AWS, assistant, with, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>Amazon Bedrock AgentCore Observability with Langfuse</title>
<link>https://news.jatlink.uk/3234</link>
<guid>https://news.jatlink.uk/3234</guid>
<description><![CDATA[ In this post, we explain how to integrate Langfuse observability with Amazon Bedrock AgentCore to gain deep visibility into an AI agent&#039;s performance, debug issues faster, and optimize costs. We walk through a complete implementation using Strands agents deployed on AgentCore Runtime followed by step-by-step code examples. ]]></description>
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<pubDate>Thu, 11 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Bedrock, AgentCore, Observability, with, Langfuse</media:keywords>
</item>

<item>
<title>How Harmonic Security improved their data&amp;leakage detection system with low&amp;latency fine&amp;tuned models using Amazon SageMaker, Amazon Bedrock, and Amazon Nova Pro</title>
<link>https://news.jatlink.uk/3231</link>
<guid>https://news.jatlink.uk/3231</guid>
<description><![CDATA[ This post walks through how Harmonic Security used Amazon SageMaker AI, Amazon Bedrock, and Amazon Nova Pro to fine-tune a ModernBERT model, achieving low-latency, accurate, and scalable data leakage detection. ]]></description>
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<pubDate>Thu, 11 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Harmonic, Security, improved, their, data-leakage, detection, system, with, low-latency, fine-tuned, models, using, Amazon, SageMaker, Amazon, Bedrock, and, Amazon, Nova, Pro</media:keywords>
</item>

<item>
<title>How Swisscom builds enterprise agentic AI for customer support and sales using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3232</link>
<guid>https://news.jatlink.uk/3232</guid>
<description><![CDATA[ In this post, we&#039;ll show how Swisscom implemented Amazon Bedrock AgentCore to build and scale their enterprise AI agents for customer support and sales operations. As an early adopter of Amazon Bedrock in the AWS Europe Region (Zurich), Swisscom leads in enterprise AI implementation with their Chatbot Builder system and various AI initiatives. Their successful deployments include Conversational AI powered by Rasa and fine-tuned LLMs on Amazon SageMaker, and the Swisscom Swisscom myAI assistant, built to meet Swiss data protection standards. ]]></description>
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<pubDate>Thu, 11 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Swisscom, builds, enterprise, agentic, for, customer, support, and, sales, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Scaling MLflow for enterprise AI: What’s New in SageMaker AI with MLflow</title>
<link>https://news.jatlink.uk/3233</link>
<guid>https://news.jatlink.uk/3233</guid>
<description><![CDATA[ Today we’re announcing Amazon SageMaker AI with MLflow, now including a serverless capability that dynamically manages infrastructure provisioning, scaling, and operations for artificial intelligence and machine learning (AI/ML) development tasks. In this post, we explore how these new capabilities help you run large MLflow workloads—from generative AI agents to large language model (LLM) experimentation—with improved performance, automation, and security using SageMaker AI with MLflow. ]]></description>
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<pubDate>Thu, 11 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, MLflow, for, enterprise, AI:, What’s, New, SageMaker, with, MLflow</media:keywords>
</item>

<item>
<title>Implement automated smoke testing using Amazon Nova Act headless mode</title>
<link>https://news.jatlink.uk/3224</link>
<guid>https://news.jatlink.uk/3224</guid>
<description><![CDATA[ This post shows how to implement automated smoke testing using Amazon Nova Act headless mode in CI/CD pipelines. We use SauceDemo, a sample ecommerce application, as our target for demonstration. We demonstrate setting up Amazon Nova Act for headless browser automation in CI/CD environments and creating smoke tests that validate key user workflows. We then show how to implement parallel execution to maximize testing efficiency, configure GitLab CI/CD for automatic test execution on every deployment, and apply best practices for maintainable and scalable test automation. ]]></description>
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<pubDate>Wed, 10 Dec 2025 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Implement, automated, smoke, testing, using, Amazon, Nova, Act, headless, mode</media:keywords>
</item>

<item>
<title>Real&amp;world reasoning: How Amazon Nova Lite 2.0 handles complex customer support scenarios</title>
<link>https://news.jatlink.uk/3221</link>
<guid>https://news.jatlink.uk/3221</guid>
<description><![CDATA[ This post evaluates the reasoning capabilities of our latest offering in the Nova family, Amazon Nova Lite 2.0, using practical scenarios that test these critical dimensions. We compare its performance against other models in the Nova family—Lite 1.0, Micro, Pro 1.0, and Premier—to elucidate how the latest version advances reasoning quality and consistency. ]]></description>
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<pubDate>Tue, 09 Dec 2025 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Real-world, reasoning:, How, Amazon, Nova, Lite, 2.0, handles, complex, customer, support, scenarios</media:keywords>
</item>

<item>
<title>Create AI&amp;powered chat assistants for your enterprise with Amazon Quick Suite</title>
<link>https://news.jatlink.uk/3219</link>
<guid>https://news.jatlink.uk/3219</guid>
<description><![CDATA[ In this post, we show how to build chat agents in Amazon Quick Suite. We walk through a three-layer framework—identity, instructions, and knowledge—that transforms Quick Suite chat agents into intelligent enterprise AI assistants. In our example, we demonstrate how our chat agent guides feature discovery, use enterprise data to inform recommendations, and tailors solutions based on potential to impact and your team’s adoption readiness. ]]></description>
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<pubDate>Tue, 09 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Create, AI-powered, chat, assistants, for, your, enterprise, with, Amazon, Quick, Suite</media:keywords>
</item>

<item>
<title>How AWS delivers generative AI to the public sector in weeks, not years</title>
<link>https://news.jatlink.uk/3214</link>
<guid>https://news.jatlink.uk/3214</guid>
<description><![CDATA[ Experts at the Generative AI Innovation Center share several strategies to help organizations excel with generative AI. ]]></description>
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<pubDate>Mon, 08 Dec 2025 18:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, AWS, delivers, generative, the, public, sector, weeks, not, years</media:keywords>
</item>

<item>
<title>Streamline AI agent tool interactions: Connect API Gateway to AgentCore Gateway with MCP</title>
<link>https://news.jatlink.uk/3212</link>
<guid>https://news.jatlink.uk/3212</guid>
<description><![CDATA[ AgentCore Gateway now supports API GatewayAs organizations explore the possibilities of agentic applications, they continue to navigate challenges of using enterprise data as context in invocation requests to large language models (LLMs) in a manner that is secure and aligned with enterprise policies. This post covers these new capabilities and shows how to implement them. ]]></description>
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<pubDate>Mon, 08 Dec 2025 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Streamline, agent, tool, interactions:, Connect, API, Gateway, AgentCore, Gateway, with, MCP</media:keywords>
</item>

<item>
<title>Create an intelligent insurance underwriter agent powered by Amazon Nova 2 Lite and Amazon Quick Suite</title>
<link>https://news.jatlink.uk/3213</link>
<guid>https://news.jatlink.uk/3213</guid>
<description><![CDATA[ In this post, we demonstrate how to build an intelligent insurance underwriting agent that addresses three critical challenges: unifying siloed data across CRM systems and databases, providing explainable and auditable AI decisions for regulatory compliance, and enabling automated fraud detection with consistent underwriting rules. The solution combines Amazon Nova 2 Lite for transparent risk assessment, Amazon Bedrock AgentCore for managed MCP server infrastructure, and Amazon Quick Suite for natural language interactions—delivering a production-ready system that underwriters can deploy in under 30 minutes . ]]></description>
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<pubDate>Mon, 08 Dec 2025 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Create, intelligent, insurance, underwriter, agent, powered, Amazon, Nova, Lite, and, Amazon, Quick, Suite</media:keywords>
</item>

<item>
<title>S&amp;amp;P Global Data integration expands Amazon Quick Research capabilities</title>
<link>https://news.jatlink.uk/3211</link>
<guid>https://news.jatlink.uk/3211</guid>
<description><![CDATA[ Today, we are pleased to announce a new integration between Amazon Quick Research and S&amp;P Global. This integration brings both S&amp;P Global Energy news, research, and insights and S&amp;P Global Market Intelligence data to Quick Research customers in one deep research agent. In this post, we explore S&amp;P Global’s data sets and the solution architecture of the integration with Quick Research. ]]></description>
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<pubDate>Mon, 08 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>S&amp;P, Global, Data, integration, expands, Amazon, Quick, Research, capabilities</media:keywords>
</item>

<item>
<title>How Myriad Genetics achieved fast, accurate, and cost&amp;efficient document processing using the AWS open&amp;source Generative AI Intelligent Document Processing Accelerator</title>
<link>https://news.jatlink.uk/3195</link>
<guid>https://news.jatlink.uk/3195</guid>
<description><![CDATA[ In this post, we explore how Myriad Genetics partnered with the AWS Generative AI Innovation Center to transform their healthcare document processing pipeline using Amazon Bedrock and Amazon Nova foundation models, achieving 98% classification accuracy while reducing costs by 77% and processing time by 80%. We detail the technical implementation using AWS&#039;s open-source GenAI Intelligent Document Processing Accelerator, the optimization strategies for document classification and key information extraction, and the measurable business impact on Myriad&#039;s prior authorization workflows. ]]></description>
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<pubDate>Thu, 27 Nov 2025 01:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Myriad, Genetics, achieved, fast, accurate, and, cost-efficient, document, processing, using, the, AWS, open-source, Generative, Intelligent, Document, Processing, Accelerator</media:keywords>
</item>

<item>
<title>How CBRE powers unified property management search and digital assistant using Amazon Bedrock</title>
<link>https://news.jatlink.uk/3196</link>
<guid>https://news.jatlink.uk/3196</guid>
<description><![CDATA[ In this post, CBRE and AWS demonstrate how they transformed property management by building a unified search and digital assistant using Amazon Bedrock, enabling professionals to access millions of documents and multiple databases through natural language queries. The solution combines Amazon Nova Pro for SQL generation and Claude Haiku for document interactions, achieving a 67% reduction in processing time while maintaining enterprise-grade security across more than eight million documents. ]]></description>
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<pubDate>Thu, 27 Nov 2025 01:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, CBRE, powers, unified, property, management, search, and, digital, assistant, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Managed Tiered KV Cache and Intelligent Routing for Amazon SageMaker HyperPod</title>
<link>https://news.jatlink.uk/3197</link>
<guid>https://news.jatlink.uk/3197</guid>
<description><![CDATA[ In this post, we introduce Managed Tiered KV Cache and Intelligent Routing for Amazon SageMaker HyperPod, new capabilities that can reduce time to first token by up to 40% and lower compute costs by up to 25% for long context prompts and multi-turn conversations. These features automatically manage distributed KV caching infrastructure and intelligent request routing, making it easier to deploy production-scale LLM inference workloads with enterprise-grade performance while significantly reducing operational overhead. ]]></description>
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<pubDate>Thu, 27 Nov 2025 01:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Managed, Tiered, Cache, and, Intelligent, Routing, for, Amazon, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>Apply fine&amp;grained access control with Bedrock AgentCore Gateway interceptors</title>
<link>https://news.jatlink.uk/3194</link>
<guid>https://news.jatlink.uk/3194</guid>
<description><![CDATA[ We are launching a new feature: gateway interceptors for Amazon Bedrock AgentCore Gateway. This powerful new capability provides fine-grained security, dynamic access control, and flexible schema management. ]]></description>
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<pubDate>Wed, 26 Nov 2025 23:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Apply, fine-grained, access, control, with, Bedrock, AgentCore, Gateway, interceptors</media:keywords>
</item>

<item>
<title>University of California Los Angeles delivers an immersive theater experience with AWS generative AI services</title>
<link>https://news.jatlink.uk/3193</link>
<guid>https://news.jatlink.uk/3193</guid>
<description><![CDATA[ In this post, we will walk through the performance constraints and design choices by OARC and REMAP teams at UCLA, including how AWS serverless infrastructure, AWS Managed Services, and generative AI services supported the rapid design and deployment of our solution. We will also describe our use of Amazon SageMaker AI and how it can be used reliably in immersive live experiences. ]]></description>
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<pubDate>Wed, 26 Nov 2025 22:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>University, California, Los, Angeles, delivers, immersive, theater, experience, with, AWS, generative, services</media:keywords>
</item>

<item>
<title>How Condé Nast accelerated contract processing and rights analysis with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3191</link>
<guid>https://news.jatlink.uk/3191</guid>
<description><![CDATA[ In this post, we explore how Condé Nast used Amazon Bedrock and Anthropic’s Claude to accelerate their contract processing and rights analysis workstreams. The company’s extensive portfolio, spanning multiple brands and geographies, required managing an increasingly complex web of contracts, rights, and licensing agreements. ]]></description>
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<pubDate>Wed, 26 Nov 2025 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Condé, Nast, accelerated, contract, processing, and, rights, analysis, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Building AI&amp;Powered Voice Applications: Amazon Nova Sonic Telephony Integration Guide</title>
<link>https://news.jatlink.uk/3192</link>
<guid>https://news.jatlink.uk/3192</guid>
<description><![CDATA[ Available through the Amazon Bedrock bidirectional streaming API, Amazon Nova Sonic can connect to your business data and external tools and can be integrated directly with telephony systems. This post will introduce sample implementations for the most common telephony scenarios. ]]></description>
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<pubDate>Wed, 26 Nov 2025 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, AI-Powered, Voice, Applications:, Amazon, Nova, Sonic, Telephony, Integration, Guide</media:keywords>
</item>

<item>
<title>Optimizing Mobileye’s REM™ with AWS Graviton: A focus on ML inference and Triton integration</title>
<link>https://news.jatlink.uk/3189</link>
<guid>https://news.jatlink.uk/3189</guid>
<description><![CDATA[ This post is written by Chaim Rand, Principal Engineer, Pini Reisman, Software Senior Principal Engineer, and Eliyah Weinberg, Performance and Technology Innovation Engineer, at Mobileye. The Mobileye team would like to thank Sunita Nadampalli and Guy Almog from AWS for their contributions to this solution and this post. Mobileye is driving the global evolution toward […] ]]></description>
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<pubDate>Wed, 26 Nov 2025 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Optimizing, Mobileye’s, REM™, with, AWS, Graviton:, focus, inference, and, Triton, integration</media:keywords>
</item>

<item>
<title>Evaluate models with the Amazon Nova evaluation container using Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3190</link>
<guid>https://news.jatlink.uk/3190</guid>
<description><![CDATA[ This blog post introduces the new Amazon Nova model evaluation features in Amazon SageMaker AI. This release adds custom metrics support, LLM-based preference testing, log probability capture, metadata analysis, and multi-node scaling for large evaluations. ]]></description>
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<pubDate>Wed, 26 Nov 2025 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluate, models, with, the, Amazon, Nova, evaluation, container, using, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Beyond the technology: Workforce changes for AI</title>
<link>https://news.jatlink.uk/3188</link>
<guid>https://news.jatlink.uk/3188</guid>
<description><![CDATA[ In this post, we explore three essential strategies for successfully integrating AI into your organization: addressing organizational debt before it compounds, embracing distributed decision-making through the &quot;octopus organization&quot; model, and redefining management roles to align with AI-powered workflows. Organizations must invest in both technology and workforce preparation, focusing on streamlining processes, empowering teams with autonomous decision-making within defined parameters, and evolving each management layer from traditional oversight to mentorship, quality assurance, and strategic vision-setting. ]]></description>
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<pubDate>Wed, 26 Nov 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Beyond, the, technology:, Workforce, changes, for</media:keywords>
</item>

<item>
<title>Enhanced performance for Amazon Bedrock Custom Model Import</title>
<link>https://news.jatlink.uk/3187</link>
<guid>https://news.jatlink.uk/3187</guid>
<description><![CDATA[ You can now achieve significant performance improvements when using Amazon Bedrock Custom Model Import, with reduced end-to-end latency, faster time-to-first-token, and improved throughput through advanced PyTorch compilation and CUDA graph optimizations. With Amazon Bedrock Custom Model Import you can to bring your own foundation models to Amazon Bedrock for deployment and inference at scale. In this post, we introduce how to use the improvements in Amazon Bedrock Custom Model Import. ]]></description>
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<pubDate>Wed, 26 Nov 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enhanced, performance, for, Amazon, Bedrock, Custom, Model, Import</media:keywords>
</item>

<item>
<title>Amazon SageMaker AI introduces EAGLE based adaptive speculative decoding to accelerate generative AI inference</title>
<link>https://news.jatlink.uk/3186</link>
<guid>https://news.jatlink.uk/3186</guid>
<description><![CDATA[ Amazon SageMaker AI now supports EAGLE-based adaptive speculative decoding, a technique that accelerates large language model inference by up to 2.5x while maintaining output quality. In this post, we explain how to use EAGLE 2 and EAGLE 3 speculative decoding in Amazon SageMaker AI, covering the solution architecture, optimization workflows using your own datasets or SageMaker&#039;s built-in data, and benchmark results demonstrating significant improvements in throughput and latency. ]]></description>
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<pubDate>Wed, 26 Nov 2025 01:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, introduces, EAGLE, based, adaptive, speculative, decoding, accelerate, generative, inference</media:keywords>
</item>

<item>
<title>Train custom computer vision defect detection model using Amazon SageMaker</title>
<link>https://news.jatlink.uk/3185</link>
<guid>https://news.jatlink.uk/3185</guid>
<description><![CDATA[ In this post, we demonstrate how to migrate computer vision workloads from Amazon Lookout for Vision to Amazon SageMaker AI by training custom defect detection models using pre-trained models available on AWS Marketplace. We provide step-by-step guidance on labeling datasets with SageMaker Ground Truth, training models with flexible hyperparameter configurations, and deploying them for real-time or batch inference—giving you greater control and flexibility for automated quality inspection use cases. ]]></description>
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<pubDate>Tue, 25 Nov 2025 23:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Train, custom, computer, vision, defect, detection, model, using, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Practical implementation considerations to close the AI value gap</title>
<link>https://news.jatlink.uk/3184</link>
<guid>https://news.jatlink.uk/3184</guid>
<description><![CDATA[ The AWS Customer Success Center of Excellence (CS COE) helps customers get tangible value from their AWS investments. We&#039;ve seen a pattern: customers who build AI strategies that address people, process, and technology together succeed more often. In this post, we share practical considerations that can help close the AI value gap. ]]></description>
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<pubDate>Tue, 25 Nov 2025 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Practical, implementation, considerations, close, the, value, gap</media:keywords>
</item>

<item>
<title>Introducing bidirectional streaming for real&amp;time inference on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3182</link>
<guid>https://news.jatlink.uk/3182</guid>
<description><![CDATA[ We&#039;re introducing bidirectional streaming for Amazon SageMaker AI Inference, which transforms inference from a transactional exchange into a continuous conversation. This post shows you how to build and deploy a container with bidirectional streaming capability to a SageMaker AI endpoint. We also demonstrate how you can bring your own container or use our partner Deepgram&#039;s pre-built models and containers on SageMaker AI to enable bi-directional streaming feature for real-time inference. ]]></description>
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<pubDate>Tue, 25 Nov 2025 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, bidirectional, streaming, for, real-time, inference, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Warner Bros. Discovery achieves 60% cost savings and faster ML inference with AWS Graviton</title>
<link>https://news.jatlink.uk/3180</link>
<guid>https://news.jatlink.uk/3180</guid>
<description><![CDATA[ Warner Bros. Discovery (WBD) is a leading global media and entertainment company that creates and distributes the world’s most differentiated and complete portfolio of content and brands across television, film and streaming. In this post, we describe the scale of our offerings, artificial intelligence (AI)/machine learning (ML) inference infrastructure requirements for our real time recommender systems, and how we used AWS Graviton-based Amazon SageMaker AI instances for our ML inference workloads and achieved 60% cost savings and 7% to 60% latency improvements across different models. ]]></description>
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<pubDate>Tue, 25 Nov 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Warner, Bros., Discovery achieves, 60, cost, savings, and, faster, inference, with, AWS, Graviton</media:keywords>
</item>

<item>
<title>Physical AI in practice: Technical foundations that fuel human&amp;machine interactions</title>
<link>https://news.jatlink.uk/3181</link>
<guid>https://news.jatlink.uk/3181</guid>
<description><![CDATA[ In this post, we explore the complete development lifecycle of physical AI—from data collection and model training to edge deployment—and examine how these intelligent systems learn to understand, reason, and interact with the physical world through continuous feedback loops. We illustrate this workflow through Diligent Robotics&#039; Moxi, a mobile manipulation robot that has completed over 1.2 million deliveries in hospitals, saving nearly 600,000 hours for clinical staff while transforming healthcare logistics and returning valuable time to patient care. ]]></description>
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<pubDate>Tue, 25 Nov 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Physical, practice:, Technical, foundations, that, fuel, human-machine, interactions</media:keywords>
</item>

<item>
<title>HyperPod now supports Multi&amp;Instance GPU to maximize GPU utilization for generative AI tasks</title>
<link>https://news.jatlink.uk/3179</link>
<guid>https://news.jatlink.uk/3179</guid>
<description><![CDATA[ In this post, we explore how Amazon SageMaker HyperPod now supports NVIDIA Multi-Instance GPU (MIG) technology, enabling you to partition powerful GPUs into multiple isolated instances for running concurrent workloads like inference, research, and interactive development. By maximizing GPU utilization and reducing wasted resources, MIG helps organizations optimize costs while maintaining performance isolation and predictable quality of service across diverse machine learning tasks. ]]></description>
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<pubDate>Tue, 25 Nov 2025 17:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>HyperPod, now, supports, Multi-Instance, GPU, maximize, GPU, utilization, for, generative, tasks</media:keywords>
</item>

<item>
<title>Accelerate generative AI innovation in Canada with Amazon Bedrock cross&amp;Region inference</title>
<link>https://news.jatlink.uk/3175</link>
<guid>https://news.jatlink.uk/3175</guid>
<description><![CDATA[ We are excited to announce that customers in Canada can now access advanced foundation models including Anthropic&#039;s Claude Sonnet 4.5 and Claude Haiku 4.5 on Amazon Bedrock through cross-Region inference (CRIS). This post explores how Canadian organizations can use cross-Region inference profiles from the Canada (Central) Region to access the latest foundation models to accelerate AI initiatives. We will demonstrate how to get started with these new capabilities, provide guidance for migrating from older models, and share recommended practices for quota management. ]]></description>
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<pubDate>Tue, 25 Nov 2025 00:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, generative, innovation, Canada, with, Amazon, Bedrock, cross-Region, inference</media:keywords>
</item>

<item>
<title>Power up your ML workflows with interactive IDEs on SageMaker HyperPod</title>
<link>https://news.jatlink.uk/3174</link>
<guid>https://news.jatlink.uk/3174</guid>
<description><![CDATA[ Amazon SageMaker HyperPod clusters with Amazon Elastic Kubernetes Service (EKS) orchestration now support creating and managing interactive development environments such as JupyterLab and open source Visual Studio Code, streamlining the ML development lifecycle by providing managed environments for familiar tools to data scientists. This post shows how HyperPod administrators can configure Spaces for their clusters, and how data scientists can create and connect to these Spaces. ]]></description>
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<pubDate>Mon, 24 Nov 2025 22:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Power, your, workflows, with, interactive, IDEs, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>Claude Opus 4.5 now in Amazon Bedrock</title>
<link>https://news.jatlink.uk/3173</link>
<guid>https://news.jatlink.uk/3173</guid>
<description><![CDATA[ Anthropic&#039;s newest foundation model, Claude Opus 4.5, is now available in Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models from leading AI companies. In this post, I&#039;ll show you what makes this model different, walk through key business applications, and demonstrate how to use Opus 4.5&#039;s new tool use capabilities on Amazon Bedrock. ]]></description>
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<pubDate>Mon, 24 Nov 2025 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Claude, Opus, 4.5, now, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Deploy GPT&amp;OSS models with Amazon Bedrock Custom Model Import</title>
<link>https://news.jatlink.uk/3171</link>
<guid>https://news.jatlink.uk/3171</guid>
<description><![CDATA[ In this post, we show how to deploy the GPT-OSS-20B model on Amazon Bedrock using Custom Model Import while maintaining complete API compatibility with your current applications. ]]></description>
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<pubDate>Mon, 24 Nov 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, GPT-OSS, models, with, Amazon, Bedrock, Custom, Model, Import</media:keywords>
</item>

<item>
<title>Streamline AI operations with the Multi&amp;Provider Generative AI Gateway reference architecture</title>
<link>https://news.jatlink.uk/3170</link>
<guid>https://news.jatlink.uk/3170</guid>
<description><![CDATA[ In this post, we introduce the Multi-Provider Generative AI Gateway reference architecture, which provides guidance for deploying LiteLLM into an AWS environment to streamline the management and governance of production generative AI workloads across multiple model providers. This centralized gateway solution addresses common enterprise challenges including provider fragmentation, decentralized governance, operational complexity, and cost management by offering a unified interface that supports Amazon Bedrock, Amazon SageMaker AI, and external providers while maintaining comprehensive security, monitoring, and control capabilities. ]]></description>
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<pubDate>Fri, 21 Nov 2025 21:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Streamline, operations, with, the, Multi-Provider, Generative, Gateway, reference, architecture</media:keywords>
</item>

<item>
<title>Deploy geospatial agents with Foursquare Spatial H3 Hub and Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3169</link>
<guid>https://news.jatlink.uk/3169</guid>
<description><![CDATA[ In this post, you&#039;ll learn how to deploy geospatial AI agents that can answer complex spatial questions in minutes instead of months. By combining Foursquare Spatial H3 Hub&#039;s analysis-ready geospatial data with reasoning models deployed on Amazon SageMaker AI, you can build agents that enable nontechnical domain experts to perform sophisticated spatial analysis through natural language queries—without requiring geographic information system (GIS) expertise or custom data engineering pipelines. ]]></description>
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<pubDate>Fri, 21 Nov 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, geospatial, agents, with, Foursquare, Spatial, Hub, and, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>How Wipro PARI accelerates PLC code generation using Amazon Bedrock</title>
<link>https://news.jatlink.uk/3168</link>
<guid>https://news.jatlink.uk/3168</guid>
<description><![CDATA[ In this post, we share how Wipro implemented advanced prompt engineering techniques, custom validation logic, and automated code rectification to streamline the development of industrial automation code at scale using Amazon Bedrock. We walk through the architecture along with the key use cases, explain core components and workflows, and share real-world results that show the transformative impact on manufacturing operations. ]]></description>
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<pubDate>Fri, 21 Nov 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Wipro, PARI, accelerates, PLC, code, generation, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>MSD explores applying generative Al to improve the deviation management process using AWS services</title>
<link>https://news.jatlink.uk/3164</link>
<guid>https://news.jatlink.uk/3164</guid>
<description><![CDATA[ This blog post has explores how MSD is harnessing the power of generative AI and databases to optimize and transform its manufacturing deviation management process. By creating an accurate and multifaceted knowledge base of past events, deviations, and findings, the company aims to significantly reduce the time and effort required for each new case while maintaining the highest standards of quality and compliance. ]]></description>
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<pubDate>Thu, 20 Nov 2025 19:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>MSD, explores, applying, generative, improve, the, deviation, management, process, using, AWS, services</media:keywords>
</item>

<item>
<title>Accelerating genomics variant interpretation with AWS HealthOmics and Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3165</link>
<guid>https://news.jatlink.uk/3165</guid>
<description><![CDATA[ In this blog post, we show you how agentic workflows can accelerate the processing and interpretation of genomics pipelines at scale with a natural language interface. We demonstrate a comprehensive genomic variant interpreter agent that combines automated data processing with intelligent analysis to address the entire workflow from raw VCF file ingestion to conversational query interfaces. ]]></description>
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<pubDate>Thu, 20 Nov 2025 19:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, genomics, variant, interpretation, with, AWS, HealthOmics, and, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>How Rufus scales conversational shopping experiences to millions of Amazon customers with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3166</link>
<guid>https://news.jatlink.uk/3166</guid>
<description><![CDATA[ Our team at Amazon builds Rufus, an AI-powered shopping assistant which delivers intelligent, conversational experiences to delight our customers. More than 250 million customers have used Rufus this year. Monthly users are up 140% YoY and interactions are up 210% YoY. Additionally, customers that use Rufus during a shopping journey are 60% more likely to […] ]]></description>
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<pubDate>Thu, 20 Nov 2025 19:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Rufus, scales, conversational, shopping, experiences, millions, Amazon, customers, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Care Access achieved 86% data processing cost reductions and 66% faster data processing with Amazon Bedrock prompt caching</title>
<link>https://news.jatlink.uk/3162</link>
<guid>https://news.jatlink.uk/3162</guid>
<description><![CDATA[ In this post, we demonstrate how healthcare organizations can securely implement prompt caching technology to streamline medical record processing while maintaining compliance requirements. ]]></description>
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<pubDate>Thu, 20 Nov 2025 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Care, Access, achieved, 86, data, processing, cost, reductions, and, 66, faster, data, processing, with, Amazon, Bedrock, prompt, caching</media:keywords>
</item>

<item>
<title>Claude Code deployment patterns and best practices with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3158</link>
<guid>https://news.jatlink.uk/3158</guid>
<description><![CDATA[ In this post, we explore deployment patterns and best practices for Claude Code with Amazon Bedrock, covering authentication methods, infrastructure decisions, and monitoring strategies to help enterprises deploy securely at scale. We recommend using Direct IdP integration for authentication, a dedicated AWS account for infrastructure, and OpenTelemetry with CloudWatch dashboards for comprehensive monitoring to ensure secure access, capacity management, and visibility into costs and developer productivity . ]]></description>
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<pubDate>Thu, 20 Nov 2025 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Claude, Code, deployment, patterns, and, best, practices, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Amazon Bedrock Guardrails expands support for code domain</title>
<link>https://news.jatlink.uk/3157</link>
<guid>https://news.jatlink.uk/3157</guid>
<description><![CDATA[ Amazon Bedrock Guardrails now extends its safety controls to protect code generation across twelve programming languages, addressing critical security challenges in AI-assisted software development. In this post, we explore how to configure content filters, prompt attack detection, denied topics, and sensitive information filters to safeguard against threats like prompt injection, data exfiltration, and malicious code generation while maintaining developer productivity . ]]></description>
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<pubDate>Wed, 19 Nov 2025 23:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Bedrock, Guardrails, expands, support, for, code, domain</media:keywords>
</item>

<item>
<title>Announcing the AWS Well&amp;Architected Responsible AI Lens </title>
<link>https://news.jatlink.uk/3156</link>
<guid>https://news.jatlink.uk/3156</guid>
<description><![CDATA[ Today, we&#039;re announcing the AWS Well-Architected Responsible AI Lens—a set of thoughtful questions and corresponding best practices that help builders address responsible AI concerns throughout development and operation. ]]></description>
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<pubDate>Wed, 19 Nov 2025 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Announcing, the, AWS, Well-Architected, Responsible, Lens </media:keywords>
</item>

<item>
<title>How Amazon uses AI agents to support compliance screening of billions of transactions per day</title>
<link>https://news.jatlink.uk/3154</link>
<guid>https://news.jatlink.uk/3154</guid>
<description><![CDATA[ Amazon&#039;s AI-powered Amazon Compliance Screening system tackles complex compliance challenges through autonomous agents that analyze, reason through, and resolve cases with precision. This blog post explores how Amazon’s Compliance team built its AI-powered investigation system through a series of AI agents built on AWS. ]]></description>
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<pubDate>Wed, 19 Nov 2025 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Amazon, uses, agents, support, compliance, screening, billions, transactions, per, day</media:keywords>
</item>

<item>
<title>Build an agentic solution with Amazon Nova, Snowflake, and LangGraph</title>
<link>https://news.jatlink.uk/3148</link>
<guid>https://news.jatlink.uk/3148</guid>
<description><![CDATA[ In this post, we cover how you can use tools from Snowflake AI Data Cloud and Amazon Web Services (AWS) to build generative AI solutions that organizations can use to make data-driven decisions, increase operational efficiency, and ultimately gain a competitive edge. ]]></description>
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<pubDate>Wed, 19 Nov 2025 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, agentic, solution, with, Amazon, Nova, Snowflake, and, LangGraph</media:keywords>
</item>

<item>
<title>Using Spectrum fine&amp;tuning to improve FM training efficiency on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3147</link>
<guid>https://news.jatlink.uk/3147</guid>
<description><![CDATA[ In this post you will learn how to use Spectrum to optimize resource use and shorten training times without sacrificing quality, as well as how to implement Spectrum fine-tuning with Amazon SageMaker AI training jobs. We will also discuss the tradeoff between QLoRA and Spectrum fine-tuning, showing that while QLoRA is more resource efficient, Spectrum results in higher performance overall. ]]></description>
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<pubDate>Wed, 19 Nov 2025 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Using, Spectrum, fine-tuning, improve, training, efficiency, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Bringing tic&amp;tac&amp;toe to life with AWS AI services</title>
<link>https://news.jatlink.uk/3145</link>
<guid>https://news.jatlink.uk/3145</guid>
<description><![CDATA[ RoboTic-Tac-Toe is an interactive game where two physical robots move around a tic-tac-toe board, with both the gameplay and robots’ movements orchestrated by LLMs. Players can control the robots using natural language commands, directing them to place their markers on the game board. In this post, we explore the architecture and prompt engineering techniques used to reason about a tic-tac-toe game and decide the next best game strategy and movement plan for the current player. ]]></description>
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<pubDate>Tue, 18 Nov 2025 23:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Bringing, tic-tac-toe, life, with, AWS, services</media:keywords>
</item>

<item>
<title>Accelerating generative AI applications with a platform engineering approach</title>
<link>https://news.jatlink.uk/3144</link>
<guid>https://news.jatlink.uk/3144</guid>
<description><![CDATA[ In this post, I will illustrate how applying platform engineering principles to generative AI unlocks faster time-to-value, cost control, and scalable innovation. ]]></description>
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<pubDate>Tue, 18 Nov 2025 18:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, generative, applications, with, platform, engineering, approach</media:keywords>
</item>

<item>
<title>HyperPod enhances ML infrastructure with security and storage</title>
<link>https://news.jatlink.uk/3143</link>
<guid>https://news.jatlink.uk/3143</guid>
<description><![CDATA[ This blog post introduces two major enhancements to Amazon SageMaker HyperPod that strengthen security and storage capabilities for large-scale machine learning infrastructure. The new features include customer managed key (CMK) support for encrypting EBS volumes with organization-controlled encryption keys, and Amazon EBS CSI driver integration that enables dynamic storage management for Kubernetes volumes in AI workloads. ]]></description>
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<pubDate>Tue, 18 Nov 2025 18:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>HyperPod, enhances, infrastructure, with, security, and, storage</media:keywords>
</item>

<item>
<title>Your complete guide to Amazon Quick Suite at AWS re:Invent 2025</title>
<link>https://news.jatlink.uk/3140</link>
<guid>https://news.jatlink.uk/3140</guid>
<description><![CDATA[ This year, re:Invent will be held in Las Vegas, Nevada, from December 1 to December 5, 2025, and this guide will help you navigate our comprehensive session catalog and plan your week. The sessions cater to business and technology leaders, product and engineering teams, and data and analytics teams interested in incorporating agentic AI capabilities across their teams and organization. ]]></description>
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<pubDate>Mon, 17 Nov 2025 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Your, complete, guide, Amazon, Quick, Suite, AWS, re:Invent, 2025</media:keywords>
</item>

<item>
<title>Accelerate enterprise solutions with agentic AI&amp;powered consulting: Introducing AWS Professional Service Agents</title>
<link>https://news.jatlink.uk/3141</link>
<guid>https://news.jatlink.uk/3141</guid>
<description><![CDATA[ I&#039;m excited to announce AWS Professional Services now offers specialized AI agents including the AWS Professional Services Delivery Agent. This represents a transformation to the consulting experience that embeds intelligent agents throughout the consulting life cycle to deliver better value for customers. ]]></description>
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<pubDate>Mon, 17 Nov 2025 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, enterprise, solutions, with, agentic, AI-powered, consulting:, Introducing, AWS, Professional, Service, Agents</media:keywords>
</item>

<item>
<title>Amazon Bedrock AgentCore and Claude: Transforming business with agentic AI</title>
<link>https://news.jatlink.uk/3139</link>
<guid>https://news.jatlink.uk/3139</guid>
<description><![CDATA[ In this post, we explore how Amazon Bedrock AgentCore and Claude are enabling enterprises like Cox Automotive and Druva to deploy production-ready agentic AI systems that deliver measurable business value, with results including up to 63% autonomous issue resolution and 58% faster response times. We examine the technical foundation combining Claude&#039;s frontier AI capabilities with AgentCore&#039;s enterprise-grade infrastructure that allows organizations to focus on agent logic rather than building complex operational systems from scratch. ]]></description>
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<pubDate>Mon, 17 Nov 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Bedrock, AgentCore, and, Claude:, Transforming, business, with, agentic</media:keywords>
</item>

<item>
<title>Build a biomedical research agent with Biomni tools and Amazon Bedrock AgentCore Gateway</title>
<link>https://news.jatlink.uk/3135</link>
<guid>https://news.jatlink.uk/3135</guid>
<description><![CDATA[ In this post, we demonstrate how to build a production-ready biomedical research agent by integrating Biomni&#039;s specialized tools with Amazon Bedrock AgentCore Gateway, enabling researchers to access over 30 biomedical databases through a secure, scalable infrastructure. The implementation showcases how to transform research prototypes into enterprise-grade systems with persistent memory, semantic tool discovery, and comprehensive observability for scientific reproducibility . ]]></description>
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<pubDate>Fri, 14 Nov 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, biomedical, research, agent, with, Biomni, tools, and, Amazon, Bedrock, AgentCore, Gateway</media:keywords>
</item>

<item>
<title>Make your web apps hands&amp;free with Amazon Nova Sonic</title>
<link>https://news.jatlink.uk/3136</link>
<guid>https://news.jatlink.uk/3136</guid>
<description><![CDATA[ Graphical user interfaces have carried the torch for decades, but today’s users increasingly expect to talk to their applications. In this post we show how we added a true voice-first experience to a reference application—the Smart Todo App—turning routine task management into a fluid, hands-free conversation. ]]></description>
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<pubDate>Fri, 14 Nov 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Make, your, web, apps, hands-free, with, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>Harnessing the power of generative AI: Druva’s multi&amp;agent copilot for streamlined data protection</title>
<link>https://news.jatlink.uk/3137</link>
<guid>https://news.jatlink.uk/3137</guid>
<description><![CDATA[ Generative AI is transforming the way businesses interact with their customers and revolutionizing conversational interfaces for complex IT operations. Druva, a leading provider of data security solutions, is at the forefront of this transformation. In collaboration with Amazon Web Services (AWS), Druva is developing a cutting-edge generative AI-powered multi-agent copilot that aims to redefine the customer experience in data security and cyber resilience. ]]></description>
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<pubDate>Fri, 14 Nov 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Harnessing, the, power, generative, AI:, Druva’s, multi-agent, copilot, for, streamlined, data, protection</media:keywords>
</item>

<item>
<title>Introducing agent&amp;to&amp;agent protocol support in Amazon Bedrock AgentCore Runtime</title>
<link>https://news.jatlink.uk/3124</link>
<guid>https://news.jatlink.uk/3124</guid>
<description><![CDATA[ In this post, we demonstrate how you can use the A2A protocol for AI agents built with different frameworks to collaborate seamlessly. You&#039;ll learn how to deploy A2A servers on AgentCore Runtime, configure agent discovery and authentication, and build a real-world multi-agent system for incident response. We&#039;ll cover the complete A2A request lifecycle, from agent card discovery to task delegation, showing how standardized protocols eliminate the complexity of multi-agent coordination. ]]></description>
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<pubDate>Tue, 11 Nov 2025 22:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, agent-to-agent, protocol, support, Amazon, Bedrock, AgentCore, Runtime</media:keywords>
</item>

<item>
<title>Powering enterprise search with the Cohere Embed 4 multimodal embeddings model in Amazon Bedrock</title>
<link>https://news.jatlink.uk/3125</link>
<guid>https://news.jatlink.uk/3125</guid>
<description><![CDATA[ The Cohere Embed 4 multimodal embeddings model is now available as a fully managed, serverless option in Amazon Bedrock. In this post, we dive into the benefits and unique capabilities of Embed 4 for enterprise search use cases. We’ll show you how to quickly get started using Embed 4 on Amazon Bedrock, taking advantage of integrations with Strands Agents, S3 Vectors, and Amazon Bedrock AgentCore to build powerful agentic retrieval-augmented generation (RAG) workflows. ]]></description>
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<pubDate>Tue, 11 Nov 2025 22:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Powering, enterprise, search, with, the, Cohere, Embed, multimodal, embeddings, model, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Multi&amp;Agent collaboration patterns with Strands Agents and Amazon Nova</title>
<link>https://news.jatlink.uk/3123</link>
<guid>https://news.jatlink.uk/3123</guid>
<description><![CDATA[ In this post, we explore four key collaboration patterns for multi-agent, multimodal AI systems – Agents as Tools, Swarms Agents, Agent Graphs, and Agent Workflows – and discuss when and how to apply each using the open-source AWS Strands Agents SDK with Amazon Nova models. ]]></description>
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<pubDate>Tue, 11 Nov 2025 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Multi-Agent, collaboration, patterns, with, Strands, Agents, and, Amazon, Nova</media:keywords>
</item>

<item>
<title>A guide to building AI agents in GxP environments</title>
<link>https://news.jatlink.uk/3122</link>
<guid>https://news.jatlink.uk/3122</guid>
<description><![CDATA[ The regulatory landscape for GxP compliance is evolving to address the unique characteristics of AI. Traditional Computer System Validation (CSV) approaches, often with uniform validation strategies, are being supplemented by Computer Software Assurance (CSA) frameworks that emphasize flexible risk-based validation methods tailored to each system&#039;s actual impact and complexity (FDA latest guidance). In this post, we cover a risk-based implementation, practical implementation considerations across different risk levels, the AWS shared responsibility model for compliance, and concrete examples of risk mitigation strategies. ]]></description>
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<pubDate>Tue, 11 Nov 2025 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>guide, building, agents, GxP, environments</media:keywords>
</item>

<item>
<title>Fine&amp;tune VLMs for multipage document&amp;to&amp;JSON with SageMaker AI and SWIFT</title>
<link>https://news.jatlink.uk/3121</link>
<guid>https://news.jatlink.uk/3121</guid>
<description><![CDATA[ In this post, we demonstrate that fine-tuning VLMs provides a powerful and flexible approach to automate and significantly enhance document understanding capabilities. We also demonstrate that using focused fine-tuning allows smaller, multi-modal models to compete effectively with much larger counterparts (98% accuracy with Qwen2.5 VL 3B). ]]></description>
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<pubDate>Mon, 10 Nov 2025 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Fine-tune, VLMs, for, multipage, document-to-JSON, with, SageMaker, and, SWIFT</media:keywords>
</item>

<item>
<title>How Clario automates clinical research analysis using generative AI on AWS</title>
<link>https://news.jatlink.uk/3120</link>
<guid>https://news.jatlink.uk/3120</guid>
<description><![CDATA[ In this post, we demonstrate how Clario has used Amazon Bedrock and other AWS services to build an AI-powered solution that automates and improves the analysis of COA interviews. ]]></description>
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<pubDate>Mon, 10 Nov 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Clario, automates, clinical, research, analysis, using, generative, AWS</media:keywords>
</item>

<item>
<title>Connect Amazon Bedrock agents to cross&amp;account knowledge bases</title>
<link>https://news.jatlink.uk/3118</link>
<guid>https://news.jatlink.uk/3118</guid>
<description><![CDATA[ Organizations need seamless access to their structured data repositories to power intelligent AI agents. However, when these resources span multiple AWS accounts integration challenges can arise. This post explores a practical solution for connecting Amazon Bedrock agents to knowledge bases in Amazon Redshift clusters residing in different AWS accounts. ]]></description>
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<pubDate>Sat, 08 Nov 2025 00:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Connect, Amazon, Bedrock, agents, cross-account, knowledge, bases</media:keywords>
</item>

<item>
<title>Democratizing AI: How Thomson Reuters Open Arena supports no&amp;code AI for every professional with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3117</link>
<guid>https://news.jatlink.uk/3117</guid>
<description><![CDATA[ In this blog post, we explore how TR addressed key business use cases with Open Arena, a highly scalable and flexible no-code AI solution powered by Amazon Bedrock and other AWS services such as Amazon OpenSearch Service, Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB, and AWS Lambda. We&#039;ll explain how TR used AWS services to build this solution, including how the architecture was designed, the use cases it solves, and the business profiles that use it. ]]></description>
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<pubDate>Fri, 07 Nov 2025 22:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Democratizing, AI:, How, Thomson, Reuters, Open, Arena, supports, no-code, for, every, professional, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Introducing structured output for Custom Model Import in Amazon Bedrock</title>
<link>https://news.jatlink.uk/3115</link>
<guid>https://news.jatlink.uk/3115</guid>
<description><![CDATA[ Today, we are excited to announce the addition of structured output to Custom Model Import. Structured output constrains a model&#039;s generation process in real time so that every token it produces conforms to a schema you define. Rather than relying on prompt-engineering tricks or brittle post-processing scripts, you can now generate structured outputs directly at inference time. ]]></description>
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<pubDate>Fri, 07 Nov 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, structured, output, for, Custom, Model, Import, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Transform your MCP architecture: Unite MCP servers through AgentCore Gateway</title>
<link>https://news.jatlink.uk/3111</link>
<guid>https://news.jatlink.uk/3111</guid>
<description><![CDATA[ Earlier this year, we introduced Amazon Bedrock AgentCore Gateway, a fully managed service that serves as a centralized MCP tool server, providing a unified interface where agents can discover, access, and invoke tools. Today, we&#039;re extending support for existing MCP servers as a new target type in AgentCore Gateway. With this capability, you can group multiple task-specific MCP servers aligned to agent goals behind a single, manageable MCP gateway interface. This reduces the operational complexity of maintaining separate gateways, while providing the same centralized tool and authentication management that existed for REST APIs and AWS Lambda functions. ]]></description>
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<pubDate>Thu, 06 Nov 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Transform, your, MCP, architecture:, Unite, MCP, servers, through, AgentCore, Gateway</media:keywords>
</item>

<item>
<title>How Amazon Search increased ML training twofold using AWS Batch for Amazon SageMaker Training jobs</title>
<link>https://news.jatlink.uk/3107</link>
<guid>https://news.jatlink.uk/3107</guid>
<description><![CDATA[ In this post, we show you how Amazon Search optimized GPU instance utilization by leveraging AWS Batch for SageMaker Training jobs. This managed solution enabled us to orchestrate machine learning (ML) training workloads on GPU-accelerated instance families like P5, P4, and others. We will also provide a step-by-step walkthrough of the use case implementation. ]]></description>
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<pubDate>Wed, 05 Nov 2025 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Amazon, Search, increased, training, twofold, using, AWS, Batch, for, Amazon, SageMaker, Training, jobs</media:keywords>
</item>

<item>
<title>Iterate faster with Amazon Bedrock AgentCore Runtime direct code deployment</title>
<link>https://news.jatlink.uk/3104</link>
<guid>https://news.jatlink.uk/3104</guid>
<description><![CDATA[ Amazon Bedrock AgentCore is an agentic platform for building, deploying, and operating effective agents securely at scale. Amazon Bedrock AgentCore Runtime is a fully managed service of Bedrock AgentCore, which provides low latency serverless environments to deploy agents and tools. It provides session isolation, supports multiple agent frameworks including popular open-source frameworks, and handles multimodal […] ]]></description>
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<pubDate>Tue, 04 Nov 2025 19:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Iterate, faster, with, Amazon, Bedrock, AgentCore, Runtime, direct, code, deployment</media:keywords>
</item>

<item>
<title>How Switchboard, MD automates real&amp;time call transcription in clinical contact centers with Amazon Nova Sonic</title>
<link>https://news.jatlink.uk/3102</link>
<guid>https://news.jatlink.uk/3102</guid>
<description><![CDATA[ In this post, we examine the specific challenges Switchboard, MD faced with scaling transcription accuracy and cost-effectiveness in clinical environments, their evaluation process for selecting the right transcription solution, and the technical architecture they implemented using Amazon Connect and Amazon Kinesis Video Streams. This post details the impressive results achieved and demonstrates how they were able to use this foundation to automate EMR matching and give healthcare staff more time to focus on patient care. ]]></description>
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<pubDate>Mon, 03 Nov 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Switchboard, automates, real-time, call, transcription, clinical, contact, centers, with, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>Build reliable AI systems with Automated Reasoning on Amazon Bedrock – Part 1</title>
<link>https://news.jatlink.uk/3100</link>
<guid>https://news.jatlink.uk/3100</guid>
<description><![CDATA[ Enterprises in regulated industries often need mathematical certainty that every AI response complies with established policies and domain knowledge. Regulated industries can’t use traditional quality assurance methods that test only a statistical sample of AI outputs and make probabilistic assertions about compliance. When we launched Automated Reasoning checks in Amazon Bedrock Guardrails in preview at […] ]]></description>
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<pubDate>Fri, 31 Oct 2025 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, reliable, systems, with, Automated, Reasoning, Amazon, Bedrock, –, Part</media:keywords>
</item>

<item>
<title>Custom Intelligence: Building AI that matches your business DNA</title>
<link>https://news.jatlink.uk/3099</link>
<guid>https://news.jatlink.uk/3099</guid>
<description><![CDATA[ In 2024, we launched the Custom Model Program within the AWS Generative AI Innovation Center to provide comprehensive support throughout every stage of model customization and optimization. Over the past two years, this program has delivered exceptional results by partnering with global enterprises and startups across diverse industries—including legal, financial services, healthcare and life sciences, […] ]]></description>
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<pubDate>Fri, 31 Oct 2025 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Custom, Intelligence:, Building, that, matches, your, business, DNA</media:keywords>
</item>

<item>
<title>Clario streamlines clinical trial software configurations using Amazon Bedrock</title>
<link>https://news.jatlink.uk/3098</link>
<guid>https://news.jatlink.uk/3098</guid>
<description><![CDATA[ This post builds upon our previous post discussing how Clario developed an AI solution powered by Amazon Bedrock to accelerate clinical trials. Since then, Clario has further enhanced their AI capabilities, focusing on innovative solutions that streamline the generation of software configurations and artifacts for clinical trials while delivering high-quality clinical evidence. ]]></description>
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<pubDate>Fri, 31 Oct 2025 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Clario, streamlines, clinical, trial, software, configurations, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Introducing Amazon Bedrock cross&amp;Region inference for Claude Sonnet 4.5 and Haiku 4.5 in Japan and Australia</title>
<link>https://news.jatlink.uk/3097</link>
<guid>https://news.jatlink.uk/3097</guid>
<description><![CDATA[ こんにちは, G’day. The recent launch of Anthropic’s Claude Sonnet 4.5 and Claude Haiku 4.5, now available on Amazon Bedrock, marks a significant leap forward in generative AI models. These state-of-the-art models excel at complex agentic tasks, coding, and enterprise workloads, offering enhanced capabilities to developers. Along with the new models, we are thrilled to announce that […] ]]></description>
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<pubDate>Fri, 31 Oct 2025 15:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Amazon, Bedrock, cross-Region, inference, for, Claude, Sonnet, 4.5, and, Haiku, 4.5, Japan, and, Australia</media:keywords>
</item>

<item>
<title>Reduce CAPTCHAs for AI agents browsing the web with Web Bot Auth (Preview) in Amazon Bedrock AgentCore Browser</title>
<link>https://news.jatlink.uk/3096</link>
<guid>https://news.jatlink.uk/3096</guid>
<description><![CDATA[ AI agents need to browse the web on your behalf. When your agent visits a website to gather information, complete a form, or verify data, it encounters the same defenses designed to stop unwanted bots: CAPTCHAs, rate limits, and outright blocks. Today, we are excited to share that AWS has a solution. Amazon Bedrock AgentCore […] ]]></description>
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<pubDate>Thu, 30 Oct 2025 22:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reduce, CAPTCHAs, for, agents, browsing, the, web, with, Web, Bot, Auth, Preview, Amazon, Bedrock, AgentCore, Browser</media:keywords>
</item>

<item>
<title>Hosting NVIDIA speech NIM models on Amazon SageMaker AI: Parakeet ASR</title>
<link>https://news.jatlink.uk/3089</link>
<guid>https://news.jatlink.uk/3089</guid>
<description><![CDATA[ In this post, we explore how to deploy NVIDIA&#039;s Parakeet ASR model on Amazon SageMaker AI using asynchronous inference endpoints to create a scalable, cost-effective pipeline for processing large volumes of audio data. The solution combines state-of-the-art speech recognition capabilities with AWS managed services like Lambda, S3, and Bedrock to automatically transcribe audio files and generate intelligent summaries, enabling organizations to unlock valuable insights from customer calls, meeting recordings, and other audio content at scale . ]]></description>
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<pubDate>Tue, 28 Oct 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Hosting, NVIDIA, speech, NIM, models, Amazon, SageMaker, AI:, Parakeet, ASR</media:keywords>
</item>

<item>
<title>Responsible AI design in healthcare and life sciences</title>
<link>https://news.jatlink.uk/3081</link>
<guid>https://news.jatlink.uk/3081</guid>
<description><![CDATA[ In this post, we explore the critical design considerations for building responsible AI systems in healthcare and life sciences, focusing on establishing governance mechanisms, transparency artifacts, and security measures that ensure safe and effective generative AI applications. The discussion covers essential policies for mitigating risks like confabulation and bias while promoting trust, accountability, and patient safety throughout the AI development lifecycle. ]]></description>
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<pubDate>Fri, 24 Oct 2025 19:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Responsible, design, healthcare, and, life, sciences</media:keywords>
</item>

<item>
<title>Beyond pilots: A proven framework for scaling AI to production</title>
<link>https://news.jatlink.uk/3080</link>
<guid>https://news.jatlink.uk/3080</guid>
<description><![CDATA[ In this post, we explore the Five V&#039;s Framework—a field-tested methodology that has helped 65% of AWS Generative AI Innovation Center customer projects successfully transition from concept to production, with some launching in just 45 days. The framework provides a structured approach through Value, Visualize, Validate, Verify, and Venture phases, shifting focus from &quot;What can AI do?&quot; to &quot;What do we need AI to do?&quot; while ensuring solutions deliver measurable business outcomes and sustainable operational excellence. ]]></description>
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<pubDate>Fri, 24 Oct 2025 16:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Beyond, pilots:, proven, framework, for, scaling, production</media:keywords>
</item>

<item>
<title>Generate Gremlin queries using Amazon Bedrock models</title>
<link>https://news.jatlink.uk/3078</link>
<guid>https://news.jatlink.uk/3078</guid>
<description><![CDATA[ In this post, we explore an innovative approach that converts natural language to Gremlin queries using Amazon Bedrock models such as Amazon Nova Pro, helping business analysts and data scientists access graph databases without requiring deep technical expertise. The methodology involves three key steps: extracting graph knowledge, structuring the graph similar to text-to-SQL processing, and generating executable Gremlin queries through an iterative refinement process that achieved 74.17% overall accuracy in testing. ]]></description>
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<pubDate>Thu, 23 Oct 2025 22:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Generate, Gremlin, queries, using, Amazon, Bedrock, models</media:keywords>
</item>

<item>
<title>Incorporating responsible AI into generative AI project prioritization</title>
<link>https://news.jatlink.uk/3079</link>
<guid>https://news.jatlink.uk/3079</guid>
<description><![CDATA[ In this post, we explore how companies can systematically incorporate responsible AI practices into their generative AI project prioritization methodology to better evaluate business value against costs while addressing novel risks like hallucination and regulatory compliance. The post demonstrates through a practical example how conducting upfront responsible AI risk assessments can significantly change project rankings by revealing substantial mitigation work that affects overall project complexity and timeline. ]]></description>
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<pubDate>Thu, 23 Oct 2025 22:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Incorporating, responsible, into, generative, project, prioritization</media:keywords>
</item>

<item>
<title>Build scalable creative solutions for product teams with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3072</link>
<guid>https://news.jatlink.uk/3072</guid>
<description><![CDATA[ In this post, we explore how product teams can leverage Amazon Bedrock and AWS services to transform their creative workflows through generative AI, enabling rapid content iteration across multiple formats while maintaining brand consistency and compliance. The solution demonstrates how teams can deploy a scalable generative AI application that accelerates everything from product descriptions and marketing copy to visual concepts and video content, significantly reducing time to market while enhancing creative quality. ]]></description>
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<pubDate>Thu, 23 Oct 2025 01:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, scalable, creative, solutions, for, product, teams, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Streamline code migration using Amazon Nova Premier with an agentic workflow</title>
<link>https://news.jatlink.uk/3070</link>
<guid>https://news.jatlink.uk/3070</guid>
<description><![CDATA[ In this post, we demonstrate how Amazon Nova Premier with Amazon Bedrock can systematically migrate legacy C code to modern Java/Spring applications using an intelligent agentic workflow that breaks down complex conversions into specialized agent roles. The solution reduces migration time and costs while improving code quality through automated validation, security assessment, and iterative refinement processes that handle even large codebases exceeding token limitations. ]]></description>
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<pubDate>Wed, 22 Oct 2025 20:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Streamline, code, migration, using, Amazon, Nova, Premier, with, agentic, workflow</media:keywords>
</item>

<item>
<title>Build a proactive AI cost management system for Amazon Bedrock – Part 2</title>
<link>https://news.jatlink.uk/3068</link>
<guid>https://news.jatlink.uk/3068</guid>
<description><![CDATA[ In this post, we explore advanced cost monitoring strategies for Amazon Bedrock deployments, introducing granular custom tagging approaches for precise cost allocation and comprehensive reporting mechanisms that build upon the proactive cost management foundation established in Part 1. The solution demonstrates how to implement invocation-level tagging, application inference profiles, and integration with AWS Cost Explorer to create a complete 360-degree view of generative AI usage and expenses. ]]></description>
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<pubDate>Wed, 22 Oct 2025 20:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, proactive, cost, management, system, for, Amazon, Bedrock, –, Part</media:keywords>
</item>

<item>
<title>Build a proactive AI cost management system for Amazon Bedrock – Part 1</title>
<link>https://news.jatlink.uk/3069</link>
<guid>https://news.jatlink.uk/3069</guid>
<description><![CDATA[ In this post, we introduce a comprehensive solution for proactively managing Amazon Bedrock inference costs through a cost sentry mechanism designed to establish and enforce token usage limits, providing organizations with a robust framework for controlling generative AI expenses. The solution uses serverless workflows and native Amazon Bedrock integration to deliver a predictable, cost-effective approach that aligns with organizational financial constraints while preventing runaway costs through leading indicators and real-time budget enforcement. ]]></description>
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<pubDate>Wed, 22 Oct 2025 20:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, proactive, cost, management, system, for, Amazon, Bedrock, –, Part</media:keywords>
</item>

<item>
<title>Metagenomi generates millions of novel enzymes cost&amp;effectively using AWS Inferentia</title>
<link>https://news.jatlink.uk/3067</link>
<guid>https://news.jatlink.uk/3067</guid>
<description><![CDATA[ In this post, we detail how Metagenomi partnered with AWS to implement the Progen2 protein language model on AWS Inferentia, achieving up to 56% cost reduction for high-throughput enzyme generation workflows. The implementation enabled cost-effective generation of millions of novel enzyme variants using EC2 Inf2 Spot Instances and AWS Batch, demonstrating how cloud-based generative AI can make large-scale protein design more accessible for biotechnology applications . ]]></description>
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<pubDate>Wed, 22 Oct 2025 15:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Metagenomi, generates, millions, novel, enzymes, cost-effectively, using, AWS, Inferentia</media:keywords>
</item>

<item>
<title>Serverless deployment for your Amazon SageMaker Canvas models</title>
<link>https://news.jatlink.uk/3064</link>
<guid>https://news.jatlink.uk/3064</guid>
<description><![CDATA[ In this post, we walk through how to take an ML model built in SageMaker Canvas and deploy it using SageMaker Serverless Inference, helping you go from model creation to production-ready predictions quickly and efficiently without managing any infrastructure. This solution demonstrates a complete workflow from adding your trained model to the SageMaker Model Registry through creating serverless endpoint configurations and deploying endpoints that automatically scale based on demand . ]]></description>
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<pubDate>Tue, 21 Oct 2025 21:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Serverless, deployment, for, your, Amazon, SageMaker, Canvas, models</media:keywords>
</item>

<item>
<title>Accelerate large&amp;scale AI training with Amazon SageMaker HyperPod training operator </title>
<link>https://news.jatlink.uk/3063</link>
<guid>https://news.jatlink.uk/3063</guid>
<description><![CDATA[ In this post, we demonstrate how to deploy and manage machine learning training workloads using the Amazon SageMaker HyperPod training operator, which enhances training resilience for Kubernetes workloads through pinpoint recovery and customizable monitoring capabilities. The Amazon SageMaker HyperPod training operator helps accelerate generative AI model development by efficiently managing distributed training across large GPU clusters, offering benefits like centralized training process monitoring, granular process recovery, and hanging job detection that can reduce recovery times from tens of minutes to seconds. ]]></description>
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<pubDate>Tue, 21 Oct 2025 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, large-scale, training, with, Amazon, SageMaker, HyperPod, training, operator </media:keywords>
</item>

<item>
<title>Building a multi&amp;agent voice assistant with Amazon Nova Sonic and Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3062</link>
<guid>https://news.jatlink.uk/3062</guid>
<description><![CDATA[ In this post, we explore how Amazon Nova Sonic&#039;s speech-to-speech capabilities can be combined with Amazon Bedrock AgentCore to create sophisticated multi-agent voice assistants that break complex tasks into specialized, manageable components. The approach demonstrates how to build modular, scalable voice applications using a banking assistant example with dedicated sub-agents for authentication, banking inquiries, and mortgage services, offering a more maintainable alternative to monolithic voice assistant designs. ]]></description>
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<pubDate>Tue, 21 Oct 2025 19:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, multi-agent, voice, assistant, with, Amazon, Nova, Sonic, and, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Beyond vibes: How to properly select the right LLM for the right task</title>
<link>https://news.jatlink.uk/3059</link>
<guid>https://news.jatlink.uk/3059</guid>
<description><![CDATA[ In this post, we discuss an approach that can guide you to build comprehensive and empirically driven evaluations that can help you make better decisions when selecting the right model for your task. ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2025/10/14/ml-19279-11.png" length="49398" type="image/jpeg"/>
<pubDate>Fri, 17 Oct 2025 18:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Beyond, vibes:, How, properly, select, the, right, LLM, for, the, right, task</media:keywords>
</item>

<item>
<title>Splash Music transforms music generation using AWS Trainium and Amazon SageMaker HyperPod</title>
<link>https://news.jatlink.uk/3060</link>
<guid>https://news.jatlink.uk/3060</guid>
<description><![CDATA[ In this post, we show how Splash Music is setting a new standard for AI-powered music creation by using its advanced HummingLM model with AWS Trainium on Amazon SageMaker HyperPod. As a selected startup in the 2024 AWS Generative AI Accelerator, Splash Music collaborated closely with AWS Startups and the AWS Generative AI Innovation Center (GenAIIC) to fast-track innovation and accelerate their music generation FM development lifecycle. ]]></description>
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<pubDate>Fri, 17 Oct 2025 18:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Splash, Music, transforms, music, generation, using, AWS, Trainium, and, Amazon, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>How TP ICAP transformed CRM data into real&amp;time insights with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3057</link>
<guid>https://news.jatlink.uk/3057</guid>
<description><![CDATA[ This post shows how TP ICAP used Amazon Bedrock Knowledge Bases and Amazon Bedrock Evaluations to build ClientIQ, an enterprise-grade solution with enhanced security features for extracting CRM insights using AI, delivering immediate business value. ]]></description>
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<pubDate>Fri, 17 Oct 2025 18:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, ICAP, transformed, CRM, data, into, real-time, insights, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Principal Financial Group accelerates build, test, and deployment of Amazon Lex V2 bots through automation</title>
<link>https://news.jatlink.uk/3058</link>
<guid>https://news.jatlink.uk/3058</guid>
<description><![CDATA[ In the post Principal Financial Group increases Voice Virtual Assistant performance using Genesys, Amazon Lex, and Amazon QuickSight, we discussed the overall Principal Virtual Assistant solution using Genesys Cloud, Amazon Lex V2, multiple AWS services, and a custom reporting and analytics solution using Amazon QuickSight. ]]></description>
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<pubDate>Fri, 17 Oct 2025 18:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Principal, Financial, Group, accelerates, build, test, and, deployment, Amazon, Lex, bots, through, automation</media:keywords>
</item>

<item>
<title>Iterative fine&amp;tuning on Amazon Bedrock for strategic model improvement</title>
<link>https://news.jatlink.uk/3056</link>
<guid>https://news.jatlink.uk/3056</guid>
<description><![CDATA[ Organizations often face challenges when implementing single-shot fine-tuning approaches for their generative AI models. The single-shot fine-tuning method involves selecting training data, configuring hyperparameters, and hoping the results meet expectations without the ability to make incremental adjustments. Single-shot fine-tuning frequently leads to suboptimal results and requires starting the entire process from scratch when improvements are […] ]]></description>
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<pubDate>Fri, 17 Oct 2025 01:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Iterative, fine-tuning, Amazon, Bedrock, for, strategic, model, improvement</media:keywords>
</item>

<item>
<title>Voice AI&amp;powered drive&amp;thru ordering with Amazon Nova Sonic and dynamic menu displays</title>
<link>https://news.jatlink.uk/3054</link>
<guid>https://news.jatlink.uk/3054</guid>
<description><![CDATA[ In this post, we&#039;ll demonstrate how to implement a Quick Service Restaurants (QSRs) drive-thru solution using Amazon Nova Sonic and AWS services. We&#039;ll walk through building an intelligent system that combines voice AI with interactive menu displays, providing technical insights and implementation guidance to help restaurants modernize their drive-thru operations. ]]></description>
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<pubDate>Thu, 16 Oct 2025 20:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Voice, AI-powered, drive-thru, ordering, with, Amazon, Nova, Sonic, and, dynamic, menu, displays</media:keywords>
</item>

<item>
<title>Optimizing document AI and structured outputs by fine&amp;tuning Amazon Nova Models and on&amp;demand inference</title>
<link>https://news.jatlink.uk/3055</link>
<guid>https://news.jatlink.uk/3055</guid>
<description><![CDATA[ This post provides a comprehensive hands-on guide to fine-tune Amazon Nova Lite for document processing tasks, with a focus on tax form data extraction. Using our open-source GitHub repository code sample, we demonstrate the complete workflow from data preparation to model deployment.  ]]></description>
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<pubDate>Thu, 16 Oct 2025 20:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Optimizing, document, and, structured, outputs, fine-tuning, Amazon, Nova, Models, and, on-demand, inference</media:keywords>
</item>

<item>
<title>Building smarter AI agents: AgentCore long&amp;term memory deep dive</title>
<link>https://news.jatlink.uk/3052</link>
<guid>https://news.jatlink.uk/3052</guid>
<description><![CDATA[ In this post, we explore how Amazon Bedrock AgentCore Memory transforms raw conversational data into persistent, actionable knowledge through sophisticated extraction, consolidation, and retrieval mechanisms that mirror human cognitive processes. The system tackles the complex challenge of building AI agents that don&#039;t just store conversations but extract meaningful insights, merge related information across time, and maintain coherent memory stores that enable truly context-aware interactions. ]]></description>
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<pubDate>Wed, 15 Oct 2025 20:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, smarter, agents:, AgentCore, long-term, memory, deep, dive</media:keywords>
</item>

<item>
<title>Transforming enterprise operations: Four high&amp;impact use cases with Amazon Nova</title>
<link>https://news.jatlink.uk/3051</link>
<guid>https://news.jatlink.uk/3051</guid>
<description><![CDATA[ In this post, we share four high-impact, widely adopted use cases built with Nova in Amazon Bedrock, supported by real-world customers deployments, offerings available from AWS partners, and experiences. These examples are ideal for organizations researching their own AI adoption strategies and use cases across industries. ]]></description>
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<pubDate>Wed, 15 Oct 2025 20:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Transforming, enterprise, operations:, Four, high-impact, use, cases, with, Amazon, Nova</media:keywords>
</item>

<item>
<title>Scala development in Amazon SageMaker Studio with Almond kernel</title>
<link>https://news.jatlink.uk/3050</link>
<guid>https://news.jatlink.uk/3050</guid>
<description><![CDATA[ This post provides a comprehensive guide on integrating the Almond kernel into SageMaker Studio, offering a solution for Scala development within the platform. ]]></description>
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<pubDate>Wed, 15 Oct 2025 18:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scala, development, Amazon, SageMaker, Studio, with, Almond, kernel</media:keywords>
</item>

<item>
<title>Configure and verify a distributed training cluster with AWS Deep Learning Containers on Amazon EKS</title>
<link>https://news.jatlink.uk/3049</link>
<guid>https://news.jatlink.uk/3049</guid>
<description><![CDATA[ Misconfiguration issues in distributed training with Amazon EKS can be prevented following a systematic approach to launch required components and verify their proper configuration. This post walks through the steps to set up and verify an EKS cluster for training large models using DLCs. ]]></description>
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<pubDate>Wed, 15 Oct 2025 18:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Configure, and, verify, distributed, training, cluster, with, AWS, Deep, Learning, Containers, Amazon, EKS</media:keywords>
</item>

<item>
<title>Build a device management agent with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3046</link>
<guid>https://news.jatlink.uk/3046</guid>
<description><![CDATA[ In this post, we explore how to build a conversational device management system using Amazon Bedrock AgentCore. With this solution, users can manage their IoT devices through natural language, using a UI for tasks like checking device status, configuring WiFi networks, and monitoring user activity. ]]></description>
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<pubDate>Tue, 14 Oct 2025 18:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, device, management, agent, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>How Amazon Bedrock Custom Model Import streamlined LLM deployment for Salesforce</title>
<link>https://news.jatlink.uk/3047</link>
<guid>https://news.jatlink.uk/3047</guid>
<description><![CDATA[ This post shows how Salesforce integrated Amazon Bedrock Custom Model Import into their machine learning operations (MLOps) workflow, reused existing endpoints without application changes, and benchmarked scalability. We share key metrics on operational efficiency and cost optimization gains, and offer practical insights for simplifying your deployment strategy. ]]></description>
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<pubDate>Tue, 14 Oct 2025 18:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Amazon, Bedrock, Custom, Model, Import, streamlined, LLM, deployment, for, Salesforce</media:keywords>
</item>

<item>
<title>Transforming the physical world with AI: the next frontier in intelligent automation </title>
<link>https://news.jatlink.uk/3044</link>
<guid>https://news.jatlink.uk/3044</guid>
<description><![CDATA[ In this post, we explore how Physical AI represents the next frontier in intelligent automation, where artificial intelligence transcends digital boundaries to perceive, understand, and manipulate the tangible world around us. ]]></description>
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<pubDate>Tue, 14 Oct 2025 00:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Transforming, the, physical, world, with, AI:, the, next, frontier, intelligent, automation </media:keywords>
</item>

<item>
<title>Medical reports analysis dashboard using Amazon Bedrock, LangChain, and Streamlit</title>
<link>https://news.jatlink.uk/3043</link>
<guid>https://news.jatlink.uk/3043</guid>
<description><![CDATA[ In this post, we demonstrate the development of a conceptual Medical Reports Analysis Dashboard that combines Amazon Bedrock AI capabilities, LangChain&#039;s document processing, and Streamlit&#039;s interactive visualization features. The solution transforms complex medical data into accessible insights through a context-aware chat system powered by large language models available through Amazon Bedrock and dynamic visualizations of health parameters. ]]></description>
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<pubDate>Mon, 13 Oct 2025 22:00:02 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Medical, reports, analysis, dashboard, using, Amazon, Bedrock, LangChain, and, Streamlit</media:keywords>
</item>

<item>
<title>Kitsa transforms clinical trial site selection with Amazon Quick Automate</title>
<link>https://news.jatlink.uk/3041</link>
<guid>https://news.jatlink.uk/3041</guid>
<description><![CDATA[ In this post, we&#039;ll show how Kitsa, a health-tech company specializing in AI-driven clinical trial recruitment and site selection, used Amazon Quick Automate to transform their clinical trial site selection solution. Amazon Quick Automate, a capability of Amazon Quick Suite, enables enterprises to build, deploy and maintain resilient workflow automations at scale. ]]></description>
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<pubDate>Mon, 13 Oct 2025 19:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Kitsa, transforms, clinical, trial, site, selection, with, Amazon, Quick, Automate</media:keywords>
</item>

<item>
<title>Connect Amazon Quick Suite to enterprise apps and agents with MCP</title>
<link>https://news.jatlink.uk/3042</link>
<guid>https://news.jatlink.uk/3042</guid>
<description><![CDATA[ In this post, we explore how Amazon Quick Suite&#039;s Model Context Protocol (MCP) client enables secure, standardized connections to enterprise applications and AI agents, eliminating the need for complex custom integrations. You&#039;ll discover how to set up MCP Actions integrations with popular enterprise tools like Atlassian Jira and Confluence, AWS Knowledge MCP Server, and Amazon Bedrock AgentCore Gateway to create a collaborative environment where people and AI agents can seamlessly work together across your organization&#039;s data and applications. ]]></description>
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<pubDate>Mon, 13 Oct 2025 19:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Connect, Amazon, Quick, Suite, enterprise, apps, and, agents, with, MCP</media:keywords>
</item>

<item>
<title>Make agents a reality with Amazon Bedrock AgentCore: Now generally available</title>
<link>https://news.jatlink.uk/3040</link>
<guid>https://news.jatlink.uk/3040</guid>
<description><![CDATA[ Learn why customers choose AgentCore to build secure, reliable AI solutions using their choice of frameworks and models for production workloads. ]]></description>
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<pubDate>Mon, 13 Oct 2025 16:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Make, agents, reality, with, Amazon, Bedrock, AgentCore:, Now, generally, available</media:keywords>
</item>

<item>
<title>Use Amazon SageMaker HyperPod and Anyscale for next&amp;generation distributed computing</title>
<link>https://news.jatlink.uk/3036</link>
<guid>https://news.jatlink.uk/3036</guid>
<description><![CDATA[ In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for distributed AI workloads with high-performance hardware, continuous monitoring, and seamless integration with Ray, the leading AI compute engine, enabling organizations to reduce time-to-market and lower total cost of ownership. ]]></description>
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<pubDate>Thu, 09 Oct 2025 23:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Use, Amazon, SageMaker, HyperPod, and, Anyscale, for, next-generation, distributed, computing</media:keywords>
</item>

<item>
<title>Customizing text content moderation with Amazon Nova</title>
<link>https://news.jatlink.uk/3037</link>
<guid>https://news.jatlink.uk/3037</guid>
<description><![CDATA[ In this post, we introduce Amazon Nova customization for text content moderation through Amazon SageMaker AI, enabling organizations to fine-tune models for their specific moderation needs. The evaluation across three benchmarks shows that customized Nova models achieve an average improvement of 7.3% in F1 scores compared to the baseline Nova Lite, with individual improvements ranging from 4.2% to 9.2% across different content moderation tasks. ]]></description>
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<pubDate>Thu, 09 Oct 2025 23:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Customizing, text, content, moderation, with, Amazon, Nova</media:keywords>
</item>

<item>
<title>Vxceed builds the perfect sales pitch for sales teams at scale using Amazon Bedrock</title>
<link>https://news.jatlink.uk/3034</link>
<guid>https://news.jatlink.uk/3034</guid>
<description><![CDATA[ In this post, we show how Vxceed used Amazon Bedrock to develop this AI-powered multi-agent solution that generates personalized sales pitches for field sales teams at scale. ]]></description>
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<pubDate>Wed, 08 Oct 2025 18:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Vxceed, builds, the, perfect, sales, pitch, for, sales, teams, scale, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Implement a secure MLOps platform based on Terraform and GitHub</title>
<link>https://news.jatlink.uk/3033</link>
<guid>https://news.jatlink.uk/3033</guid>
<description><![CDATA[ Machine learning operations (MLOps) is the combination of people, processes, and technology to productionize ML use cases efficiently. To achieve this, enterprise customers must develop MLOps platforms to support reproducibility, robustness, and end-to-end observability of the ML use case’s lifecycle. Those platforms are based on a multi-account setup by adopting strict security constraints, development best […] ]]></description>
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<pubDate>Wed, 08 Oct 2025 17:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Implement, secure, MLOps, platform, based, Terraform, and, GitHub</media:keywords>
</item>

<item>
<title>Implement automated monitoring for Amazon Bedrock batch inference</title>
<link>https://news.jatlink.uk/3031</link>
<guid>https://news.jatlink.uk/3031</guid>
<description><![CDATA[ In this post, we demonstrated how a financial services company can use an FM to process large volumes of customer records and get specific data-driven product recommendations. We also showed how to implement an automated monitoring solution for Amazon Bedrock batch inference jobs. By using EventBridge, Lambda, and DynamoDB, you can gain real-time visibility into batch processing operations, so you can efficiently generate personalized product recommendations based on customer credit data. ]]></description>
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<pubDate>Tue, 07 Oct 2025 19:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Implement, automated, monitoring, for, Amazon, Bedrock, batch, inference</media:keywords>
</item>

<item>
<title>Automate Amazon QuickSight data stories creation with agentic AI using Amazon Nova Act</title>
<link>https://news.jatlink.uk/3030</link>
<guid>https://news.jatlink.uk/3030</guid>
<description><![CDATA[ In this post, we demonstrate how Amazon Nova Act automates QuickSight data story creation, saving time so you can focus on making critical, data-driven business decisions. ]]></description>
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<pubDate>Tue, 07 Oct 2025 19:00:02 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automate, Amazon, QuickSight, data, stories, creation, with, agentic, using, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Responsible AI: How PowerSchool safeguards millions of students with AI&amp;powered content filtering using Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3028</link>
<guid>https://news.jatlink.uk/3028</guid>
<description><![CDATA[ In this post, we demonstrate how PowerSchool built and deployed a custom content filtering solution using Amazon SageMaker AI that achieved better accuracy while maintaining low false positive rates. We walk through our technical approach to fine tuning Llama 3.1 8B, our deployment architecture, and the performance results from internal validations. ]]></description>
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<pubDate>Mon, 06 Oct 2025 21:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Responsible, AI:, How, PowerSchool, safeguards, millions, students, with, AI-powered, content, filtering, using, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Unlock global AI inference scalability using new global cross&amp;Region inference on Amazon Bedrock  with Anthropic’s Claude Sonnet 4.5</title>
<link>https://news.jatlink.uk/3022</link>
<guid>https://news.jatlink.uk/3022</guid>
<description><![CDATA[ Organizations are increasingly integrating generative AI capabilities into their applications to enhance customer experiences, streamline operations, and drive innovation. As generative AI workloads continue to grow in scale and importance, organizations face new challenges in maintaining consistent performance, reliability, and availability of their AI-powered applications. Customers are looking to scale their AI inference workloads across […] ]]></description>
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<pubDate>Fri, 03 Oct 2025 23:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unlock, global, inference, scalability, using, new, global, cross-Region, inference, Amazon, Bedrock, with, Anthropic’s, Claude, Sonnet, 4.5</media:keywords>
</item>

<item>
<title>Secure ingress connectivity to Amazon Bedrock AgentCore Gateway using interface VPC endpoints</title>
<link>https://news.jatlink.uk/3021</link>
<guid>https://news.jatlink.uk/3021</guid>
<description><![CDATA[ In this post, we demonstrate how to access AgentCore Gateway through a VPC interface endpoint from an Amazon Elastic Compute Cloud (Amazon EC2) instance in a VPC. We also show how to configure your VPC endpoint policy to provide secure access to the AgentCore Gateway while maintaining the principle of least privilege access. ]]></description>
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<pubDate>Fri, 03 Oct 2025 22:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Secure, ingress, connectivity, Amazon, Bedrock, AgentCore, Gateway, using, interface, VPC, endpoints</media:keywords>
</item>

<item>
<title>Enhance agentic workflows with enterprise search using Kore.ai and Amazon Q Business</title>
<link>https://news.jatlink.uk/3020</link>
<guid>https://news.jatlink.uk/3020</guid>
<description><![CDATA[ In this post, we demonstrate how organizations can enhance their employee productivity by integrating Kore.ai’s AI for Work platform with Amazon Q Business. We show how to configure AI for Work as a data accessor for Amazon Q index for independent software vendors (ISVs), so employees can search enterprise knowledge and execute end-to-end agentic workflows involving search, reasoning, actions, and content generation. ]]></description>
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<pubDate>Fri, 03 Oct 2025 00:00:02 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enhance, agentic, workflows, with, enterprise, search, using, Kore.ai, and, Amazon, Business</media:keywords>
</item>

<item>
<title>Accelerate development with the Amazon Bedrock AgentCore MCP server</title>
<link>https://news.jatlink.uk/3019</link>
<guid>https://news.jatlink.uk/3019</guid>
<description><![CDATA[ Today, we’re excited to announce the Amazon Bedrock AgentCore Model Context Protocol (MCP) Server. With built-in support for runtime, gateway integration, identity management, and agent memory, the AgentCore MCP Server is purpose-built to speed up creation of components compatible with Bedrock AgentCore. You can use the AgentCore MCP server for rapid prototyping, production AI solutions, […] ]]></description>
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<pubDate>Thu, 02 Oct 2025 23:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, development, with, the, Amazon, Bedrock, AgentCore, MCP, server</media:keywords>
</item>

<item>
<title>Rox accelerates sales productivity with AI agents powered by Amazon Bedrock</title>
<link>https://news.jatlink.uk/3016</link>
<guid>https://news.jatlink.uk/3016</guid>
<description><![CDATA[ We’re excited to announce that Rox is generally available, with Rox infrastructure built on AWS and delivered across web, Slack, macOS, and iOS. In this post, we share how Rox accelerates sales productivity with AI agents powered by Amazon Bedrock. ]]></description>
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<pubDate>Wed, 01 Oct 2025 20:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rox, accelerates, sales, productivity, with, agents, powered, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Hapag&amp;Lloyd improved schedule reliability with ML&amp;powered vessel schedule predictions using Amazon SageMaker</title>
<link>https://news.jatlink.uk/3015</link>
<guid>https://news.jatlink.uk/3015</guid>
<description><![CDATA[ In this post, we share how Hapag-Lloyd developed and implemented a machine learning (ML)-powered assistant predicting vessel arrival and departure times that revolutionizes their schedule planning. By using Amazon SageMaker AI and implementing robust MLOps practices, Hapag-Lloyd has enhanced its schedule reliability—a key performance indicator in the industry and quality promise to their customers. ]]></description>
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<pubDate>Wed, 01 Oct 2025 20:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Hapag-Lloyd, improved, schedule, reliability, with, ML-powered, vessel, schedule, predictions, using, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Modernize fraud prevention: GraphStorm v0.5 for real&amp;time inference</title>
<link>https://news.jatlink.uk/3013</link>
<guid>https://news.jatlink.uk/3013</guid>
<description><![CDATA[ In this post, we demonstrate how to implement real-time fraud prevention using GraphStorm v0.5&#039;s new capabilities for deploying graph neural network (GNN) models through Amazon SageMaker. We show how to transition from model training to production-ready inference endpoints with minimal operational overhead, enabling sub-second fraud detection on transaction graphs with billions of nodes and edges. ]]></description>
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<pubDate>Tue, 30 Sep 2025 22:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Modernize, fraud, prevention:, GraphStorm, v0.5, for, real-time, inference</media:keywords>
</item>

<item>
<title>Building health care agents using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/2999</link>
<guid>https://news.jatlink.uk/2999</guid>
<description><![CDATA[ In this solution, we demonstrate how the user (a parent) can interact with a Strands or LangGraph agent in conversational style and get information about the immunization history and schedule of their child, inquire about the available slots, and book appointments. With some changes, AI agents can be made event-driven so that they can automatically send reminders, book appointments, and so on. ]]></description>
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<pubDate>Fri, 26 Sep 2025 18:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, health, care, agents, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Build multi&amp;agent site reliability engineering assistants with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/2998</link>
<guid>https://news.jatlink.uk/2998</guid>
<description><![CDATA[ In this post, we demonstrate how to build a multi-agent SRE assistant using Amazon Bedrock AgentCore, LangGraph, and the Model Context Protocol (MCP). This system deploys specialized AI agents that collaborate to provide the deep, contextual intelligence that modern SRE teams need for effective incident response and infrastructure management. ]]></description>
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<pubDate>Fri, 26 Sep 2025 17:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, multi-agent, site, reliability, engineering, assistants, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>DoWhile loops now supported in Amazon Bedrock Flows</title>
<link>https://news.jatlink.uk/2996</link>
<guid>https://news.jatlink.uk/2996</guid>
<description><![CDATA[ Today, we are excited to announce support for DoWhile loops in Amazon Bedrock Flows. With this powerful new capability, you can create iterative, condition-based workflows directly within your Amazon Bedrock flows, using Prompt nodes, AWS Lambda functions, Amazon Bedrock Agents, Amazon Bedrock Flows inline code, Amazon Bedrock Knowledge Bases, Amazon Simple Storage Service (Amazon S3), […] ]]></description>
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<pubDate>Thu, 25 Sep 2025 22:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>DoWhile, loops, now, supported, Amazon, Bedrock, Flows</media:keywords>
</item>

<item>
<title>How PropHero built an intelligent property investment advisor with continuous evaluation using Amazon Bedrock</title>
<link>https://news.jatlink.uk/2994</link>
<guid>https://news.jatlink.uk/2994</guid>
<description><![CDATA[ In this post, we explore how we built a multi-agent conversational AI system using Amazon Bedrock that delivers knowledge-grounded property investment advice. We explore the agent architecture, model selection strategy, and comprehensive continuous evaluation system that facilitates quality conversations while facilitating rapid iteration and improvement. ]]></description>
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<pubDate>Thu, 25 Sep 2025 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, PropHero, built, intelligent, property, investment, advisor, with, continuous, evaluation, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Accelerate benefits claims processing with Amazon Bedrock Data Automation</title>
<link>https://news.jatlink.uk/2995</link>
<guid>https://news.jatlink.uk/2995</guid>
<description><![CDATA[ In the benefits administration industry, claims processing is a vital operational pillar that makes sure employees and beneficiaries receive timely benefits, such as health, dental, or disability payments, while controlling costs and adhering to regulations like HIPAA and ERISA. In this post, we examine the typical benefit claims processing workflow and identify where generative AI-powered automation can deliver the greatest impact. ]]></description>
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<pubDate>Thu, 25 Sep 2025 21:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, benefits, claims, processing, with, Amazon, Bedrock, Data, Automation</media:keywords>
</item>

<item>
<title>Running deep research AI agents on Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/2987</link>
<guid>https://news.jatlink.uk/2987</guid>
<description><![CDATA[ AI agents are evolving beyond basic single-task helpers into more powerful systems that can plan, critique, and collaborate with other agents to solve complex problems. Deep Agents—a recently introduced framework built on LangGraph—bring these capabilities to life, enabling multi-agent workflows that mirror real-world team dynamics. The challenge, however, is not just building such agents but […] ]]></description>
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<pubDate>Tue, 23 Sep 2025 22:00:03 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Running, deep, research, agents, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Integrate tokenization with Amazon Bedrock Guardrails for secure data handling</title>
<link>https://news.jatlink.uk/2986</link>
<guid>https://news.jatlink.uk/2986</guid>
<description><![CDATA[ In this post, we show you how to integrate Amazon Bedrock Guardrails with third-party tokenization services to protect sensitive data while maintaining data reversibility. By combining these technologies, organizations can implement stronger privacy controls while preserving the functionality of their generative AI applications and related systems. ]]></description>
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<pubDate>Tue, 23 Sep 2025 19:00:04 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Integrate, tokenization, with, Amazon, Bedrock, Guardrails, for, secure, data, handling</media:keywords>
</item>

<item>
<title>Rapid ML experimentation for enterprises with Amazon SageMaker AI and Comet</title>
<link>https://news.jatlink.uk/2983</link>
<guid>https://news.jatlink.uk/2983</guid>
<description><![CDATA[ In this post, we showed how to use SageMaker and Comet together to spin up fully managed ML environments with reproducibility and experiment tracking capabilities. ]]></description>
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<pubDate>Mon, 22 Sep 2025 19:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rapid, experimentation, for, enterprises, with, Amazon, SageMaker, and, Comet</media:keywords>
</item>

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