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1. MSD explores applying generative Al to improve the deviation management process using AWS services

   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...

2. Accelerating genomics variant interpretation with AWS HealthOmics and Amazon Bedrock AgentCore

   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.

3. How Rufus scales conversational shopping experiences to millions of Amazon customers with Amazon Bedrock

   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 […]

4. How Care Access achieved 86% data processing cost reductions and 66% faster data processing with Amazon Bedrock prompt caching

   In this post, we demonstrate how healthcare organizations can securely implement prompt caching technology to streamline medical record processing while maintaining compliance requirements.

5. Claude Code deployment patterns and best practices with Amazon Bedrock

   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...

6. Amazon Bedrock Guardrails expands support for code domain

   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...

7. Announcing the AWS Well-Architected Responsible AI Lens 

   Today, we'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.

8. How Amazon uses AI agents to support compliance screening of billions of transactions per day

   Amazon'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.

9. Build an agentic solution with Amazon Nova, Snowflake, and LangGraph

   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.

10. Using Spectrum fine-tuning to improve FM training efficiency on Amazon SageMaker AI

    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.