MLFlow Crash Course: MLOps in Python

A comprehensive crash course on MLflow covering both GenAI and classical ML use cases. Topics include LLM tracing with OpenAI, LangChain, and Mistral via autolog; LLM evaluation using built-in scorers (correctness, guidelines) and custom scorers; prompt template versioning and management; AI gateway setup with load balancing and fallback models; deploying agents as FastAPI endpoints; scikit-learn model tracking and registry; hyperparameter tuning with Optuna; and PyTorch training loop monitoring with system metrics and model checkpointing.