MLOps with MLflow and LangChain for LLM Applications

Managing the lifecycle of machine learning and large language model applications requires structured tracking, versioning, and evaluation. This text-based guide teaches you how to streamline your development process by combining the power of MLflow with LangChain.\n\nThrough clear written explanations and practical code walkthroughs, you will develop a solid foundation in operational machine learning workflows and LLMOps essentials.\n\nWhat you'll learn:\n- Understand fundamental MLOps and LLMOps terminology, architecture, and core principles\n- Configure MLflow to track experiments, parameters, metrics, and artifacts\n- Build modular language model chains and pipelines with LangChain\n- Apply automated logging and tracing to monitor LangChain calls in MLflow\n- Evaluate model outputs, prompt iterations, and retrieval-augmented generation results\n- Manage model registry lifecycles and organize deployment-ready components\n\nThe course starts with essential concepts and setup before moving into step-by-step implementations of experiment tracking, prompt logging, and pipeline management. Designed specifically for beginners, this course requires only basic familiarity with Python and no prior MLOps experience.\n\nBegin reading today to master the tools that make language model workflows reproducible and reliable.