Introduction to Open Source LLMOps

Deploying large language models in production requires more than just third-party API calls; it demands a robust infrastructure for managing open-source AI. In this comprehensive written course, you will transition from understanding basic AI concepts to managing and deploying open-source large language models. You will read clear explanations, study practical code snippets, and master the operational workflows needed to build reliable, self-hosted AI systems. What you'll learn: 1. Understand the core terminology of large language model architectures and open-source licensing. 2. Configure retrieval-augmented generation (RAG) systems to connect models with external data. 3. Apply fine-tuning techniques to adapt open-source models to specific domain tasks. 4. Implement modern MLOps practices for model evaluation, monitoring, and version control. 5. Deploy open-source models efficiently using containerization and optimized inference engines. 6. Practice setting up vector databases to manage semantic search and memory for your AI applications. The course begins with foundational AI concepts and architecture definitions before guiding you through deployment strategies and optimization workflows. You will read structured explanations and analyze configuration scripts designed to build your operational confidence. This course is designed for software developers, data practitioners, and technology enthusiasts who want to learn LLMOps from scratch, with no prior machine learning operations experience required. Start reading today to take full control of your open-source AI infrastructure.