The 4D Framework for LLMOps: Build Reliable AI Systems

Moving Large Language Models from a simple prototype to a reliable production system requires a structured operational framework to manage the entire lifecycle of AI-driven applications. This text-based course guides you through the 4D Framework—Discover, Distill, Deploy, and Deliver—to help you build, optimize, and maintain robust LLMOps pipelines. In this course, you will: 1. Understand the core terminology of LLMs, vector databases, and retrieval-augmented generation (RAG). 2. Discover and evaluate the right model architectures and prompt strategies for your specific business needs. 3. Distill knowledge and optimize model performance using quantization, fine-tuning, and efficient data pipelines. 4. Deploy scalable models securely using modern containerization and API integration patterns. 5. Deliver reliable AI services by setting up continuous monitoring, evaluation metrics, and cost management. You will start with foundational AI concepts and terminology before exploring each phase of the 4D lifecycle through written explanations, architectural breakdowns, and practical code walkthroughs. Designed for software engineers, data professionals, and tech enthusiasts new to LLMOps, this course requires no advanced machine learning background. Start reading today to master the structured path of production LLM operations.