Managing Local LLMs with MLflow and MLOps

Managing the lifecycle of modern language models requires structured tracking, reproducible workflows, and systematic evaluation. This course provides a structured introduction to MLOps and LLMOps, showing you how to manage local open-weight language models efficiently using MLflow. You will build a solid understanding of how to log experiments, track prompt performance, and maintain model artifacts throughout the machine learning lifecycle. By following clear written explanations and practical code examples, you will learn to apply essential operations principles to local language model projects. What you'll learn: - Understand foundational MLOps and LLMOps concepts, terminology, and lifecycle stages - Configure MLflow to track parameters, metrics, and artifacts for local language models - Log prompts, inputs, and generation outputs to compare model performance - Evaluate model quality and responses using structured evaluation metrics - Register and version models in the MLflow Model Registry for consistent lifecycle tracking - Apply modern best practices for local model inference and pipeline reproducibility The course begins with foundational definitions and architecture overviews before progressing into step-by-step walkthroughs of tracking, evaluation, and registry workflows. This course is created for beginners, data practitioners, and developers who want to manage language models systematically without needing prior MLOps experience. Read through the lessons to start organizing and tracking your local language model workflows today.