Build a Local Private AI Stack: Self-Hosted LLMs and RAG

As AI becomes essential to modern workflows, sending sensitive data to public cloud providers introduces major privacy risks. Running your own local AI stack allows your team to leverage powerful models while keeping proprietary data completely secure. This course guides you through the foundational concepts of setting up a private, self-hosted AI infrastructure. You will explore how to run Large Language Models (LLMs) locally, build secure internal API gateways, and implement Retrieval-Augmented Generation (RAG) to connect AI with your internal documents. What you'll learn: - Understand the core differences between cloud-based and local AI deployments. - Configure foundational infrastructure for self-hosting large language models. - Design a Retrieval-Augmented Generation (RAG) pipeline using modern vector databases. - Set up an API gateway to manage and route team requests to your local AI. - Apply fundamental containerization and MLOps concepts for reliable deployment. - Practice prompt engineering basics to optimize local model outputs. The course begins with essential AI terminology and architecture concepts before moving into practical deployment strategies. You will read through step-by-step written scenarios and configuration examples to understand how each component of a private AI stack connects. Designed for beginners, aspiring platform engineers, and developers looking to understand AI infrastructure, this course requires no prior machine learning experience. Start building your secure, private AI environment today.