Managing and Logging LLMs with MLflow and GGUF

Managing the lifecycle of large language models requires reliable tracking and structured artifact management. This text-based course teaches you how to organize, log, and load quantized language models such as Phi-3 in GGUF format using MLflow. You will gain a clear, practical foundation in modern MLOps practices tailored to open weights models. Starting with key terminology and core architectural concepts, you will move step-by-step through tracking experiment runs, managing model artifacts, and deploying loaded models for inference. What you'll learn: - Understand essential MLOps terminology, experiment tracking concepts, and model quantization principles - Configure MLflow tracking environments and log parameters, metrics, and tags - Save and package quantized GGUF models like Phi-3 as reproducible MLflow artifacts - Load logged models and execute local inference routines efficiently - Apply best practices for LLM versioning, metadata management, and model registries - Track prompt configurations and evaluation outputs systematically The course begins with fundamental definitions and setup instructions before progressing into structured text explanations and code examples for end-to-end model logging. Designed for beginners and data practitioners, it requires no prior MLOps experience. Start reading today to build dependable workflows for logging and managing language models.