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

Mercury foundation models from Inception Labs are now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

Favorite Today, we are excited to announce that Mercury and Mercury Coder foundation models (FMs) from Inception Labs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can deploy the Mercury FMs to build, experiment, and responsibly scale your generative AI applications on AWS. In

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Shared by AWS Machine Learning August 28, 2025

Learn how Amazon Health Services improved discovery in Amazon search using AWS ML and gen AI

Favorite Healthcare discovery on ecommerce domains presents unique challenges that traditional product search wasn’t designed to handle. Unlike searching for books or electronics, healthcare queries involve complex relationships between symptoms, conditions, treatments, and services, requiring sophisticated understanding of medical terminology and customer intent. This challenge became particularly relevant for Amazon

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Shared by AWS Machine Learning August 27, 2025

Amazon SageMaker HyperPod enhances ML infrastructure with scalability and customizability

Favorite Amazon SageMaker HyperPod is a purpose-built infrastructure for optimizing foundation model (FM) training and inference at scale. SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training FMs, reducing training time by up to 40%. SageMaker HyperPod offers persistent clusters with

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Shared by AWS Machine Learning August 23, 2025

Accelerate intelligent document processing with generative AI on AWS

Favorite Every day, organizations process millions of documents, including invoices, contracts, insurance claims, medical records, and financial statements. Despite the critical role these documents play, an estimated 80–90% of the data they contain is unstructured and largely untapped, hiding valuable insights that could transform business outcomes. Despite advances in technology,

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Shared by AWS Machine Learning August 23, 2025

Beyond the basics: A comprehensive foundation model selection framework for generative AI

Favorite Most organizations evaluating foundation models limit their analysis to three primary dimensions: accuracy, latency, and cost. While these metrics provide a useful starting point, they represent an oversimplification of the complex interplay of factors that determine real-world model performance. Foundation models have revolutionized how enterprises develop generative AI applications,

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Shared by AWS Machine Learning August 23, 2025