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. Link to article: …

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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 …

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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. Link to article: …

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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. Link to article: …

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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 …

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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. Link to article: https://aws.amazon.com/blogs/machine-learning/kick-off-nova-customization-experiments-using-nova-forge-sdk/

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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. Link to article: …

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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. Link to article: …

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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. Link …

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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 …

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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. Link …

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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. Link to article: …

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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. Link to article: …

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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&L, run enterprise architecture, lead …

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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 …

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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 …

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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, …

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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. Link to article: https://aws.amazon.com/blogs/machine-learning/p-eagle-faster-llm-inference-with-parallel-speculative-decoding-in-vllm/

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