MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It tackles three primary functions:
- Tracking experiments to record and compare parameters and results (MLflow Tracking).
- Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production (MLflow Projects).
- Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (MLflow Models).
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 REST API and CLI. For convenience, the project also includes a Python API.
Get started using the Quickstart or by reading about the key concepts.
The current version of MLflow is an alpha release. This means that APIs and storage formats are subject to breaking change.