Artificial Intelligence
Category: Amazon SageMaker Lakehouse
Build secure RAG applications with AWS serverless data lakes
In this post, we explore how to build a secure RAG application using serverless data lake architecture, an important data strategy to support generative AI development. We use Amazon Web Services (AWS) services including Amazon S3, Amazon DynamoDB, AWS Lambda, and Amazon Bedrock Knowledge Bases to create a comprehensive solution supporting unstructured data assets which can be extended to structured data. The post covers how to implement fine-grained access controls for your enterprise data and design metadata-driven retrieval systems that respect security boundaries. These approaches will help you maximize the value of your organization’s data while maintaining robust security and compliance.
Optimize RAG in production environments using Amazon SageMaker JumpStart and Amazon OpenSearch Service
In this post, we show how to use Amazon OpenSearch Service as a vector store to build an efficient RAG application.
Governing the ML lifecycle at scale, Part 4: Scaling MLOps with security and governance controls
This post provides detailed steps for setting up the key components of a multi-account ML platform. This includes configuring the ML Shared Services Account, which manages the central templates, model registry, and deployment pipelines; sharing the ML Admin and SageMaker Projects Portfolios from the central Service Catalog; and setting up the individual ML Development Accounts where data scientists can build and train models.