Build a serverless Amazon Bedrock batch job orchestration workflow using AWS Step Functions

Favorite As organizations increasingly adopt foundation models (FMs) for their artificial intelligence and machine learning (AI/ML) workloads, managing large-scale inference operations efficiently becomes crucial. Amazon Bedrock supports two general types of large-scale inference patterns: real-time inference and batch inference for use cases that involve processing massive datasets where immediate results

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Shared by AWS Machine Learning September 3, 2025

Train and deploy models on Amazon SageMaker HyperPod using the new HyperPod CLI and SDK

Favorite Training and deploying large AI models requires advanced distributed computing capabilities, but managing these distributed systems shouldn’t be complex for data scientists and machine learning (ML) practitioners. The newly released command line interface (CLI) and software development kit (SDK) for Amazon SageMaker HyperPod simplify how you can use the

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Shared by AWS Machine Learning September 3, 2025

Introducing auto scaling on Amazon SageMaker HyperPod

Favorite Today, we’re excited to announce that Amazon SageMaker HyperPod now supports managed node automatic scaling with Karpenter, so you can efficiently scale your SageMaker HyperPod clusters to meet your inference and training demands. Real-time inference workloads require automatic scaling to address unpredictable traffic patterns and maintain service level agreements

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Shared by AWS Machine Learning August 30, 2025

Set up custom domain names for Amazon Bedrock AgentCore Runtime agents

Favorite When deploying AI agents to Amazon Bedrock AgentCore Runtime (currently in preview), customers often want to use custom domain names to create a professional and seamless experience. By default, AgentCore Runtime agents use endpoints like https://bedrock-agentcore.{region}.amazonaws.com/runtimes/{EncodedAgentARN}/invocations. In this post, we discuss how to transform these endpoints into user-friendly custom

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Shared by AWS Machine Learning August 30, 2025

Detect Amazon Bedrock misconfigurations with Datadog Cloud Security

Favorite This post was co-written with Nick Frichette and Vijay George from Datadog.  As organizations increasingly adopt Amazon Bedrock for generative AI applications, protecting against misconfigurations that could lead to data leaks or unauthorized model access becomes critical. The AWS Generative AI Adoption Index, which surveyed 3,739 senior IT decision-makers

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Shared by AWS Machine Learning August 30, 2025

How Amazon Finance built an AI assistant using Amazon Bedrock and Amazon Kendra to support analysts for data discovery and business insights

Favorite Finance analysts across Amazon Finance face mounting complexity in financial planning and analysis processes. When working with vast datasets spanning multiple systems, data lakes, and business units, analysts encounter several critical challenges. First, they spend significant time manually browsing data catalogs and reconciling data from disparate sources, leaving less

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Shared by AWS Machine Learning August 29, 2025

Meet Boti: The AI assistant transforming how the citizens of Buenos Aires access government information with Amazon Bedrock

Favorite This post is co-written with Julieta Rappan, Macarena Blasi, and María Candela Blanco from the Government of the City of Buenos Aires. The Government of the City of Buenos Aires continuously works to improve citizen services. In February 2019, it introduced an AI assistant named Boti available through WhatsApp,

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Shared by AWS Machine Learning August 29, 2025