AI

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 generative […]

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Introducing OS Level Actions in Amazon Bedrock AgentCore Browser

AI agents that automate web workflows operate within the browser’s web layer, the DOM that Playwright and the Chrome DevTools Protocol (CDP) expose. AgentCore Browser provides a secure, isolated browser environment for this, and it works well for the vast majority of automation: navigating pages, filling forms, clicking elements, extracting content. But the web layer

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Secure AI agents with Amazon Bedrock AgentCore Identity on Amazon ECS

AI agents in production require secure access to external services. Amazon Bedrock AgentCore Identity, available as a standalone service, secures how your AI agents access external services whether they run on compute platforms like Amazon ECS, Amazon EKS, AWS Lambda, or on-premises. An earlier post covered AgentCore Identity credential management for AI agents. Running agents

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Intelligence-driven message defense and insights using Amazon Bedrock

Direct communication between buyers and sellers outside approved channels can result in significant revenue loss annually while severely damaging brand reputation and destroying valuable business relationships. While messaging systems are essential for modern business operations and help provide rich customer insights, they can create significant risks when parties bypass the brokerage system to communicate directly.

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Beyond BI: How the Dataset Q&A feature of Amazon Quick powers the next generation of data decisions

Business leaders across industries rely on operational dashboards as the shared source of truth that their teams execute against daily. But dashboards are built to answer known questions. When teams need to explore further, ad-hoc, multi-dimensional, or unforeseen questions, they hit a bottleneck. They wait hours or days for BI teams to build new views

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