MLflow Skinny is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. MLflow Skinny supports:
- Tracking operations (logging / loading / searching params, metrics, tags + logging / loading artifacts)
- Model registration, search, artifact loading, and deployment
- Execution of GitHub projects within notebook & against a remote target.
Additional dependencies can be installed to leverage the full feature set of MLflow. For example:
- To use the mlflow.sklearn component of MLflow Models, install scikit-learn, numpy and pandas.
- To use SQL-based metadata storage, install sqlalchemy, alembic, and sqlparse.
- To use serving-based features, install flask and pandas.