MLflow Installation Experiment Tracking Autologging Model Registry PyTorch Integration Serving Models Remote Tracking Server Key Concepts - Runs : Individual experiment executions with parameters, metrics, and artifacts - Experiments : Groups of runs for comparison and organization - Model Registry : Central hub for versioning, aliasing (staging/production), and managing models - Autologging : One-line integration for sklearn, PyTorch, TensorFlow, XGBoost, etc. - Artifacts : Files (models, plots, data) stored alongside runs for reproducibility - Model serving : Deploy any registered model as…