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  • Learnings from COBOL modernization in the real world

    calendar Feb 26, 2026 · aws.amazon.com/blogs/machine-learning
    Learnings from COBOL modernization in the real world

    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 …


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  • Reinforcement fine-tuning for Amazon Nova: Teaching AI through feedback

    calendar Feb 26, 2026 · aws.amazon.com/blogs/machine-learning
    Reinforcement fine-tuning for Amazon Nova: Teaching AI through feedback

    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'll cover how RFT works, when to use it versus supervised fine-tuning, real-world applications from code generation to customer …


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  • Large model inference container – latest capabilities and performance enhancements

    calendar Feb 26, 2026 · aws.amazon.com/blogs/machine-learning
    Large model inference container – latest capabilities and performance enhancements

    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 …


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  • Efficiently serve dozens of fine-tuned models with vLLM on Amazon SageMaker AI and Amazon Bedrock

    calendar Feb 25, 2026 · aws.amazon.com/blogs/machine-learning
    Efficiently serve dozens of fine-tuned models with vLLM on Amazon SageMaker AI and Amazon Bedrock

    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. Link to article: …


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  • Building intelligent event agents using Amazon Bedrock AgentCore and Amazon Bedrock Knowledge Bases

    calendar Feb 25, 2026 · aws.amazon.com/blogs/machine-learning
    Building intelligent event agents using Amazon Bedrock AgentCore and Amazon Bedrock Knowledge Bases

    This post demonstrates how to quickly deploy a production-ready event assistant using the components of Amazon Bedrock AgentCore. We'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 …


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  • Build an intelligent photo search using Amazon Rekognition, Amazon Neptune, and Amazon Bedrock

    calendar Feb 24, 2026 · aws.amazon.com/blogs/machine-learning
    Build an intelligent photo search using Amazon Rekognition, Amazon Neptune, and Amazon Bedrock

    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. Link to article: …


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  • Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs

    calendar Feb 24, 2026 · aws.amazon.com/blogs/machine-learning
    Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs

    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 …


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  • Generate structured output from LLMs with Dottxt Outlines in AWS

    calendar Feb 24, 2026 · aws.amazon.com/blogs/machine-learning
    Generate structured output from LLMs with Dottxt Outlines in AWS

    This post explores the implementation of Dottxt’s Outlines framework as a practical approach to implementing structured outputs using AWS Marketplace in Amazon SageMaker. Link to article: https://aws.amazon.com/blogs/machine-learning/generate-structured-output-from-llms-with-dottxt-outlines-in-aws/


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  • Global cross-Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan

    calendar Feb 24, 2026 · aws.amazon.com/blogs/machine-learning
    Global cross-Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan

    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 …


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  • Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)

    calendar Feb 24, 2026 · aws.amazon.com/blogs/machine-learning
    Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)

    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 …


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  • Scaling data annotation using vision-language models to power physical AI systems

    calendar Feb 23, 2026 · aws.amazon.com/blogs/machine-learning
    Scaling data annotation using vision-language models to power physical AI systems

    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 …


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  • How Sonrai uses Amazon SageMaker AI to accelerate precision medicine trials

    calendar Feb 23, 2026 · aws.amazon.com/blogs/machine-learning
    How Sonrai uses Amazon SageMaker AI to accelerate precision medicine trials

    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. Link to article: …


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  • Accelerating AI model production at Hexagon with Amazon SageMaker HyperPod

    calendar Feb 23, 2026 · aws.amazon.com/blogs/machine-learning
    Accelerating AI model production at Hexagon with Amazon SageMaker HyperPod

    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. Link to article: …


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  • Agentic AI with multi-model framework using Hugging Face smolagents on AWS

    calendar Feb 23, 2026 · aws.amazon.com/blogs/machine-learning
    Agentic AI with multi-model framework using Hugging Face smolagents on AWS

    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'll learn how to deploy a …


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