MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It tackles four primary functions:
- Tracking experiments to record and compare parameters and results (:ref:`tracking`).
- Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production (:ref:`projects`).
- Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (:ref:`models`).
- Providing a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations (:ref:`registry`).
MLflow is library-agnostic. You can use it with any machine learning library, and in any programming language, since all functions are accessible through a :ref:`rest-api` and :ref:`CLI<cli>`. For convenience, the project also includes a :ref:`python-api`, :ref:`R-api`, and :ref:`java_api`.
.. toctree:: :maxdepth: 1 quickstart tutorials-and-examples/index concepts tracking projects models model-registry plugins cli search-syntax python_api/index R-api java_api/index rest-api