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1. Operationalizing Agentic AI Part 1: A Stakeholder’s Guide

   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.

2. Accelerate custom LLM deployment: Fine-tune with Oumi and deploy to Amazon Bedrock

   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.

3. Run NVIDIA Nemotron 3 Nano as a fully managed serverless model on Amazon Bedrock

   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....

4. Access Anthropic Claude models in India on Amazon Bedrock with Global cross-Region inference

   In this post, you will discover how to use Amazon Bedrock'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.

5. Drive organizational growth with Amazon Lex multi-developer CI/CD pipeline

   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.

6. Building custom model provider for Strands Agents with LLMs hosted on SageMaker AI endpoints

   This post demonstrates how to build custom model parsers for Strands agents when working with LLMs hosted on SageMaker that don't natively support the Bedrock Messages API format. We'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.

7. Embed Amazon Quick Suite chat agents in enterprise applications

   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.

8. Unlock powerful call center analytics with Amazon Nova foundation models

   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.

9. How Ricoh built a scalable intelligent document processing solution on AWS

   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.

10. Building a scalable virtual try-on solution using Amazon Nova on AWS: part 1

    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.