MLflow provides tools for the ML lifecycle. Tracking logs parameters, metrics, and artifacts. Model Registry manages model versions. Projects package reproducible runs.
Experiment Tracking
Log parameters configuring runs. Track metrics during training. Store artifacts like models and visualizations. Compare runs identifying best configurations.
- Log all hyperparameters for reproducibility
- Track metrics at regular intervals during training
- Store model artifacts for later deployment
- Use tags for organizing and filtering runs
- Compare experiments in the MLflow UI
Model Management
Register models in the Model Registry. Version models through development lifecycle. Stage models from development to production. Integrate with deployment pipelines.