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1. Deploy SageMaker AI inference endpoints with set GPU capacity using training plans

   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's journey as they reserve capacity for model evaluation and manage the endpoint throughout the reservation lifecycle.

2. Accelerating custom entity recognition with Claude tool use in Amazon Bedrock

   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.

3. How Reco transforms security alerts using Amazon Bedrock

   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.

4. Integrating Amazon Bedrock AgentCore with Slack

   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's security requirements, and implement conversation management patterns that work for many agent use cases.

5. Overcoming LLM hallucinations in regulated industries: Artificial Genius’s deterministic models on Amazon Nova

   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.

6. Run NVIDIA Nemotron 3 Super on Amazon Bedrock

   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.

7. Use RAG for video generation using Amazon Bedrock and Amazon Nova Reel

   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.

8. Introducing V-RAG: revolutionizing AI-powered video production with Retrieval Augmented Generation

   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.

9. Enhanced metrics for Amazon SageMaker AI endpoints: deeper visibility for better performance

   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.

10. Enforce data residency with Amazon Quick extensions for Microsoft Teams

    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.