Mlops Workflows

This is a solid reference for building production ML systems with MLflow. It covers the full lifecycle: experiment tracking with nested runs for hyperparameter tuning, model registry with versioning and aliases, and deployment patterns. The code examples are practical, showing autologging, metric tracking over epochs, and artifact management. It's most useful when you're moving from Jupyter notebooks to repeatable workflows or need to standardize how your team tracks experiments. The nested run pattern for grid search is especially handy. Missing some depth on the monitoring and feature store sections based on the preview, but the core MLflow workflows are well documented.