Course Outline
Set up a fully offline Retrieval-Augmented Generation (RAG) solution using open-source models and Docker. Learn to deploy a local model serving pipeline with ChromaDB for vector storage, all wrapped in a Streamlit app
Learning Outcomes
- Learn to deploy and serve open-source LLM’s locally using Docker containers
- Setup a fully offline RAG pipeline with Meta Llama open source models
- Understand the challenges building and deploying an offline AI system
Who Is This Course For?
- Anyone looking at deployment of open-source models locally or closed-network solutions
- Engineers and architects looking at secure RAG solutions
- Engineers looking to familiarize themselves with open-source models
Prerequisites
- Intermediate working experience with language models or machine learning models
- Fundamental understanding of RAG
- Previously worked with Docker or any cloud deployment (we will recap some of the basics)
LevelAdvanced
Duration2 hrs
Showcasing
Chroma
Streamlit
Your InstructorInstructor
Progress0/8 chapters complete
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Your certificateComplete the course to get your certificate
Why Enroll?
- In this project, you will learn to build a fully offline RAG pipeline using open-source models. By deploying everything locally using Docker, you’ll set up model serving, vector storage with ChromaDB, and a user interface with Streamlit to run RAG workflows seamlessly. This project will help engineers who want to build self-contained AI systems, providing a robust foundation for handling sensitive data or use cases requiring offline capabilities.