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1. Cost-efficient custom text-to-SQL using Amazon Nova Micro and Amazon Bedrock on-demand inference

   In this post, we demonstrate two approaches to fine-tune Amazon Nova Micro for custom SQL dialect generation to deliver both cost efficiency and production ready performance.

2. Transform retail with AWS generative AI services

   Online retailers face a persistent challenge: shoppers struggle to determine the fit and look when ordering online, leading to increased returns and decreased purchase confidence. The cost? Lost revenue, operational overhead, and customer frustration. Meanwhile, consumers increasingly expect immersive, interactive shopping experiences that bridge the gap between online and in-store retail....

3. How Automated Reasoning checks in Amazon Bedrock transform generative AI compliance

   In this post, you'll learn why probabilistic AI validation falls short in regulated industries and how Automated Reasoning checks use formal verification to deliver mathematically proven results. You'll also see how customers across six industries use this technology to produce formally verified, auditable AI outputs, and how to get started.

4. Create rich, custom tooltips in Amazon Quick Sight

   Today, we're announcing sheet tooltips in Amazon Quick Sight. Dashboard authors can now design custom tooltip layouts using free-form layout sheets. These layouts combine charts, key performance indicator (KPI) metrics, text, and other visuals into a single tooltip that renders dynamically when readers hover over data points.

5. Accelerating decode-heavy LLM inference with speculative decoding on AWS Trainium and vLLM

   In this post, you will learn how speculative decoding works and why it helps reduce cost per generated token on AWS Trainium2.

6. Rede Mater Dei de Saúde: Monitoring AI agents in the revenue cycle with Amazon Bedrock AgentCore

   This post is cowritten by Renata Salvador Grande, Gabriel Bueno and Paulo Laurentys at Rede Mater Dei de Saúde. The growing adoption of multi-agent AI systems is redefining critical operations in healthcare. In large hospital networks, where thousands of decisions directly impact cash flow, service delivery times, and the risk of claim denials, the ability […]

7. Navigating the generative AI journey: The Path-to-Value framework from AWS

   In this post, we introduce the Generative AI Path-to-Value (P2V) framework, a structured approach to help you move generative AI initiatives from concept to production and sustained value creation.

8. Use-case based deployments on SageMaker JumpStart

   We're excited to announce the launch of Amazon SageMaker JumpStart optimized deployments. SageMaker JumpStart improved deployments address the need for rich and straightforward deployment customization on SageMaker JumpStart by offering pre-defined deployment configurations, designed for specific use cases. Customers maintain the same level of visibility into the details of their proposed...

9. Best practices to run inference on Amazon SageMaker HyperPod

   This post explores how Amazon SageMaker HyperPod provides a comprehensive solution for inference workloads. We walk you through the platform’s key capabilities for dynamic scaling, simplified deployment, and intelligent resource management. By the end of this post, you’ll understand how to use the HyperPod automated infrastructure, cost optimization features, and performance enhancements to...

10. How Guidesly built AI-generated trip reports for outdoor guides on AWS

    In this post, we walk through how Guidesly built Jack AI on AWS using AWS Lambda, AWS Step Functions, Amazon Simple Storage Service (Amazon S3), Amazon Relational Database Service (Amazon RDS), Amazon SageMaker AI, and Amazon Bedrock to ingest trip media, enrich it with context, apply computer vision and generative AI, and publish marketing-ready content across multiple channels—securely,...