Author: Sandeep Raveesh-Babu

Today we’re announcing Amazon SageMaker AI with MLflow, now including a serverless capability that dynamically manages infrastructure provisioning, scaling, and operations for artificial intelligence and machine learning (AI/ML) development tasks. In this post, we explore how these new capabilities help you run large MLflow workloads—from generative AI agents to large language model (LLM) experimentation—with improved performance, automation, and security using SageMaker AI with MLflow.

In this post, we walk through how to streamline your RAG development lifecycle from experimentation to automation, helping you operationalize your RAG solution for production deployments with Amazon SageMaker AI, helping your team experiment efficiently, collaborate effectively, and drive continuous improvement.

Organizations are adopting large language models (LLMs), such as DeepSeek R1, to transform business processes, enhance customer experiences, and drive innovation at unprecedented speed. However, standalone LLMs have key limitations such as hallucinations, outdated knowledge, and no access to proprietary data. Retrieval Augmented Generation (RAG) addresses these gaps by combining semantic search with generative AI, […]

In this post, I show you how to combine LangChain’s LangGraph, Amazon SageMaker AI, and MLflow to demonstrate a powerful workflow for developing, evaluating, and deploying sophisticated generative AI agents. This integration provides the tools needed to gain deep insights into the generative AI agent’s performance, iterate quickly, and maintain version control throughout the development process.