mlflow
Tracks machine learning experiments and manages model lifecycles with MLflow, covering mlflow.log_param, log_metric and log_artifact, autologging for scikit-learn, PyTorch Lightning, XGBoost and HuggingFace Transformers, the Model Registry with versions and stage transitions, run searching, and local or cloud model serving. Use when logging parameters, metrics and artifacts for training runs, comparing runs across experiments, registering and promoting model versions from Staging to Production, serving a logged model for inference, or reproducing an experiment from an MLflow project. Not for hyperparameter-sweep dashboards or general data versioning; use a dedicated tool for those.
- Version
- 1.0.0
- License
- MIT
Pinned to revision df088027ff23, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/mlflow/SKILL.md
- skills/mlflow/references/autologging.md
- skills/mlflow/references/deployment.md
- skills/mlflow/references/integration-examples.md
- skills/mlflow/references/model-registry-2.md
- skills/mlflow/references/model-registry.md
- skills/mlflow/references/tracking.md
Every link opens the file at its source, pinned to the revision this page describes.