Amazon SageMaker AI collection of examples, code samples and recipes.

Workshop for getting started with generative AI on Amazon SageMaker

Amazon Sagemaker Projects on Generative AI.

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generative-ai-on-amazon-sagemaker

This project takes us through the fundamentals of Generative AI using AWS in-built models like Meta, Amazon and Mistral AI on Sagemaker unified studio. Python codes written on jupyter notebook

Production-ready Generative AI & RAG solutions on AWS. Features Amazon Bedrock, SageMaker, Aurora PostgreSQL (pgvector), Lambda, and React/Amplify. Demonstrates scalable Vector Stores, Agents, and LLM orchestration.

A comprehensive learning repository documenting hands-on exploration of Generative AI, Machine Learning Engineering, and MLOps on AWS. Includes notes, architectures, experiments, and implementations using services such as Amazon Bedrock, SageMaker, vector databases, and serverless AI pipelines.

This project showcases how to build and deploy a generative AI-powered web application where users interact with large language models (LLMs) hosted on Amazon Bedrock through a serverless backend. It demonstrates end-to-end implementation, from infrastructure setup to prompt optimization using SageMaker Studio notebooks.