Category: Amazon Machine Learning

Agentic Data Ingestion is an open-source solution that uses AI agents on Amazon Bedrock to automate data pipeline creation, from file upload to queryable tables. Point it at a file or database table, and foundation models propose the field mapping, generate and test the AWS Glue transform, and run the job behind human approval gates.

Governed, multi-tenant data access is a key requirement for SaaS and B2C products that embed AI-powered analytics. This post shows how to combine Amazon Cognito and AWS Lake Formation on Amazon Bedrock AgentCore to enforce row- and column-level data access for external users, with a minimal-privilege Lambda acting as a credential broker.

AI agents can reach enterprise data where it lives instead of routing every question through data engineers. This post presents three reference patterns for federated data access using Model Context Protocol (MCP) servers and Amazon Bedrock AgentCore: catalog-first, direct source, and hybrid access.

In this post, you build a contract compliance search system that combines semantic search with semantic highlighting in Amazon OpenSearch Service. You deploy the solution using two AWS CloudFormation stacks, test it with synthetic contract documents, and see how a single query surfaces both the right contracts and the right clauses within them.

This post focuses on explaining the architecture approach to build the open lakehouse architecture on AWS, unifying the metadata catalog across providers for the AI agents to access. In addition, it highlights the architecture trade-offs and best practices.

In this post, we explore how MDAA transforms data architecture development from months of manual coding to production-ready deployment through configuration-driven infrastructure and embedded governance, examine a real customer transformation, and provide a clear implementation pathway for your own data modernization journey.

In this post, you learn how to build an AI-powered solution that collects the telemetry, pre-computes performance signals, correlates them with CloudWatch, and uses Amazon Bedrock to generate prioritized recommendations.

In this post, we explore how Nexthink combined Amazon OpenSearch Service vector search, Amazon Bedrock, and infrastructure as code to power the Spark agent’s retrieval layer.

In this post, we explore how to build an online shopping AI agent. We focus on its architecture and implementation with Amazon OpenSearch Service, Amazon Bedrock AgentCore, and Strands Agents. Amazon Bedrock AgentCore is an agentic platform for deploying and operating those agents and tools securely at scale without managing infrastructure.

Now, Amazon OpenSearch Service brings three new agentic AI features to OpenSearch UI. In this post, we show how these capabilities work together to help engineers go from alert to root cause in minutes. We also walk through a sample scenario where the Investigation Agent automatically correlates data across multiple indices to surface a root cause hypothesis.