AWS Machine Learning Blog

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Official Machine Learning Blog of Amazon Web Services

Latest posts

  1. SageMaker Inference now supports Multi-Instance GPU to maximize GPU utilization for model endpoints

    · aws.amazon.com · Shantanu Tripathi

    Amazon SageMaker Inference now supports GPU partitioning with NVIDIA Multi-Instance GPU (MIG), which divides one GPU into hardware-isolated partitions so you can run a different model in each or take a right-sized fraction of a newer accelerator. This post shows how two optional API fields place…

  2. Taxonomy of agentic primitives in Amazon Quick

    · aws.amazon.com · Thomas Delteil

    As we built Amazon Quick, its agents grew from answering questions to taking actions, and the same primitives kept getting rebuilt under different names. This post shares the taxonomy we converged on — triggers, agents, capabilities, and learned context — found by decomposing agentic work into four…

  3. Serverless MCP server for AI agent data lake access on Amazon ECS Fargate

    · aws.amazon.com · Aishwarya Pawar

    Learn how to build a serverless Model Context Protocol (MCP) server on Amazon ECS with AWS Fargate that gives AI agents secure, metadata-driven access to an enterprise data lake. Exposing a new dataset requires only a JSON file, with no per-dataset integration code.

  4. Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 2

    · aws.amazon.com · Hammad Ausaf

    In Part 1 you built one governed agent. Part 2 scales the pattern to an enterprise: Amazon Bedrock AgentCore Gateway becomes the single front door and redeems Okta's grant, Cedar policies bind each tool to the agent, a second agent runs on Amazon EKS, and AgentCore Observability traces every allow…

  5. Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 1

    · aws.amazon.com · Hammad Ausaf

    An AI agent that acts for a user raises an identity question that per-app OAuth and service accounts leave open: which agent may act for which user, and how you stop it. In this first post of two, you build a task agent on Amazon Bedrock AgentCore that governs access through Okta Cross App Access…

  6. How Greenlight Financial built an autonomous SOC solution on Amazon Bedrock AgentCore

    · aws.amazon.com · Adam Maksimuk

    Greenlight Financial resolves more than 90% of up to 4,000 monthly security and risk cases without human intervention using Luna, a tiered multi-agent system on Amazon Bedrock AgentCore. This post covers the architecture, episodic memory for continuous learning, and the expansion from SOC…

  7. Taxbit Assist: A grounded, role-aware AI agent on Amazon Bedrock AgentCore

    · aws.amazon.com · Karan Nayak

    Taxbit built Taxbit Assist, a cited, permission-aware AI assistant embedded in its enterprise dashboard, on Amazon Bedrock AgentCore. This post shows how they grounded answers in a curated knowledge base, enforced role-aware and region-compliant safety, and continuously measured answer quality on…

  8. Extend Claude Code to remote sessions on Amazon Bedrock AgentCore with security controls

    · aws.amazon.com · Andrew (Qingjie) Ren

    Run Claude Code as a remote agent on Amazon Bedrock AgentCore. Each coding task runs in its own isolated session in your AWS account, every model call goes through Amazon Bedrock, and the result comes back as a GitHub pull request you review and merge.

  9. Getting started with Web Answer on Amazon Bedrock AgentCore

    · aws.amazon.com · Adrianna Chudy

    Web Answer on Amazon Bedrock AgentCore returns a synthesized, source-cited answer to a natural-language question in a single tool call, with no search, retrieve, synthesize, and citation pipeline to build. Learn how it works and how to add it to an AgentCore Gateway and call it from an agent.

  10. How Amazon Marketplace evaluates content quality with Amazon SageMaker AI

    · aws.amazon.com · James Kim

    Learn how Amazon Marketplace evaluates A+ Content quality at scale on Amazon SageMaker AI by rendering each product page as a single snapshot, scoring it with a vision-language model fused with OCR readability signals in a CatBoost classifier, and using SHAP to turn each decision into prioritized…

  11. Use decision models with Amazon Bedrock AgentCore Evaluations

    · aws.amazon.com · Sungyeon Kim

    Some agent-evaluation questions are narrow and repeatable, and decision models answer exactly that kind of question. This post shows how to connect two decision models, Jev and Strands Decider, to Amazon Bedrock AgentCore Evaluations through code-based evaluators, and how to validate a…

  12. Smart Approvals in Amazon Quick: Human oversight where it matters

    · aws.amazon.com · Steve Vinsik

    Smart Approvals in Amazon Quick reviews eligible agent actions within your existing controls, reducing unnecessary approval prompts while keeping consequential decisions with a person. See how policy sets the boundary, how a classifier reviews actions, and how AWS CloudTrail and Amazon CloudWatch…

  13. Four layers of context engineering for production AI agents: How Deep Insight applies them in working code

    · aws.amazon.com · Yoonseo Kim

    In production AI agents, context management is the real bottleneck. This post presents a four-layer context engineering framework (architecture, prompts, file-based tools, and verification agents), implemented in working code from Deep Insight, an open source multi-agent data analysis solution on…

  14. How Indegene is modernizing and scaling enterprise data quality in life sciences

    · aws.amazon.com · Bhagyashree Chandak

    This post explores how Indegene built a production-grade, LLM-powered Data Quality Agent in its NEXT Unified Data Platform on AWS. The agent generates context-aware data quality rules, validates them, and produces ready-to-run SQL and Python under human-in-the-loop governance, helping life sciences…

  15. How U Mobile built agentic telco operations with Amazon Bedrock AgentCore

    · aws.amazon.com · Jin Tiong Lim

    U Mobile, Malaysia's newest 5G network provider, built AiDaH, an agentic AI operations platform on Amazon Bedrock AgentCore that moves beyond AI assistance to autonomous action across telco operations. Nine months after production launch, AiDaH handles over 150 operational scenarios across six…

  16. Bring structured and unstructured data together with Amazon Quick

    · aws.amazon.com · Puneeth Komaragiri

    Amazon Quick brings structured and unstructured data together in one experience. In this post, you connect Amazon S3 as a knowledge base, enrich a dataset, build a Space, and create a custom chat agent that answers business questions across both data types through a single conversational interface.

  17. ServiceNow on-behalf-of token exchange with Bedrock AgentCore Gateway

    · aws.amazon.com · Greg Biegel

    Learn how to configure Amazon Bedrock AgentCore Gateway for an on-behalf-of (OBO) token exchange with Microsoft Entra ID, so an AI agent can call the ServiceNow REST API while preserving the calling user's identity. AgentCore Gateway performs the exchange natively, with no interceptor Lambda or…

  18. How Gen Digital built Norton Revamp’s AI-curated News Engine with Amazon Bedrock AgentCore Web Search

    · aws.amazon.com · Iskander Sanchez-Rola

    Gen Digital built Norton Revamp, a generative AI-powered professional reputation management platform, with an AI-curated News Engine grounded by Web Search on Amazon Bedrock AgentCore. See how the Gen team delivers real-time, cited content suggestions, goal setting, and news feeds with zero data…

  19. Build a semantic layer for agentic AI with EDB Postgres AI and Amazon Bedrock AgentCore

    · aws.amazon.com · Rajesh Nagpal

    Learn how to build a governed semantic layer for agentic AI inside EDB Postgres AI and query it from a Strands Agents agent on Amazon Bedrock AgentCore. Business meaning, join paths, and access control all live in Postgres, with GRANT and row-level security enforcing what each agent can read.

  20. Building ultra-low-latency voice AI applications on AWS

    · aws.amazon.com · Nolan Chen

    Learn how Cartesia, an AWS Partner, uses purpose-built State Space Models and AWS infrastructure to deliver sub-100ms text-to-speech and speech-to-text at global scale, and how to deploy its Sonic model on Amazon SageMaker AI or self-managed Amazon EC2 GPU instances.

In the directory since 25 Sept 2026 · last checked 9 Nov 2026 · RSS

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