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1. Manufacturing intelligence with Amazon Nova Multimodal Embeddings

   In this post, we build a multimodal retrieval system for aerospace manufacturing documents using Amazon Nova Multimodal Embeddings on Amazon Bedrock and Amazon S3 Vectors. We evaluate the system on 26 manufacturing queries and compare generation quality between a text-only pipeline and the multimodal pipeline.

2. How Miro uses Amazon Bedrock to boost software bug routing accuracy and improve time-to-resolution from days to hours

   In this post, we dive deep into the architecture and techniques we used to improve Miro’s bug routing, achieving six times fewer team reassignments and five times shorter time-to-resolution powered by Amazon Bedrock.

3. Amazon Quick: Accelerating the path from enterprise data to AI-powered decisions

   Amazon Quick helps turn your large enterprise data into fast and accurate AI-powered decisions. In this post, you will learn about five new capabilities of Amazon Quick that accelerate how data professionals deliver trusted AI-powered insights at enterprise scale.

4. Halliburton enhances seismic workflow creation with Amazon Bedrock and Generative AI

   In this post, we'll explore how we built a proof-of-concept that converts natural language queries into executable seismic workflows while providing a question-answering capability for Halliburton's Seismic Engine tools and documentation. We'll cover the technical details of the solution, share evaluation results showing workflow acceleration of up to 95%, and discuss key learnings that can help...

5. Secure short-term GPU capacity for ML workloads with EC2 Capacity Blocks for ML and SageMaker training plans

   In this post, you will learn how to secure reserved GPU capacity for short-term workloads using Amazon Elastic Compute Cloud (Amazon EC2) Capacity Blocks for ML and Amazon SageMaker training plans. These solutions can address GPU availability challenges when you need short-term capacity for load testing, model validation, time-bound workshops, or preparing inference capacity ahead of a release.

6. Overcoming reward signal challenges: Verifiable rewards-based reinforcement learning with GRPO on SageMaker AI

   In this post, you will learn how to implement reinforcement learning with verifiable rewards (RLVR) to introduce verification and transparency into reward signals to improve training performance. This approach works best when outputs can be objectively verified for correctness, such as in mathematical reasoning, code generation, or symbolic manipulation tasks. You will also learn how to layer...

7. Agents that transact: Introducing Amazon Bedrock AgentCore payments, built with Coinbase and Stripe

   Today, we're announcing a preview of Amazon Bedrock AgentCore Payments, a new set of features in Amazon Bedrock AgentCore that enables AI agents to instantly access and pay for what they use. AgentCore Payments was developed in partnership with Coinbase and Stripe.

8. Cost effective deployment of vision-language models for pet behavior detection on AWS Inferentia2

   Tomofun, the Taiwan-headquartered pet-tech startup behind the Furbo Pet Camera, is redefining how pet owners interact with their pets remotely. To reduce costs and maintain accuracy, Tomofun turned to EC2 Inf2 instances powered by AWS Inferentia2, the Amazon purpose-built AI chips. In this post, we walk through the following sections in detail.

9. How Hapag-Lloyd uses Amazon Bedrock to transform customer feedback into actionable insights

   Hapag-Lloyd's Digital Customer Experience and Engineering team, distributed between Hamburg and Gdańsk, drives digital innovation by developing and maintaining customer-facing web and mobile products. In this post, we walk you through our generative AI–powered feedback analysis solution built using Amazon Bedrock, Elasticsearch, and open-source frameworks like LangChain and LangGraph

10. Streamlining generative AI development with MLflow v3.10 on Amazon SageMaker AI

    Today, we’re excited to announce that Amazon SageMaker AI MLflow Apps now support MLflow version 3.10, bringing enhanced capabilities for generative AI development and streamlined experiment tracking to your generative AI workflows. Building on the foundations established with Amazon SageMaker AI MLflow Apps, this latest version introduces powerful new features for observability, evaluation, and...