Artificial Intelligence
Category: Amazon SageMaker Lakehouse
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.