MLflow Guide

Note

This section describes MLflow features that are in Private Preview. To request access to the preview, contact your Azure Databricks sales representative. If you are not participating in the preview, see the MLflow open-source documentation for information on how to run standalone MLflow.

MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It has three primary components: Tracking, Models, and Projects:

  • Tracking: Allows you to track experiments to record and compare parameters and results.
  • Models: Allows you to manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms.
  • Projects: Allow you to package ML code in a reusable, reproducible form to share with other data scientists or transfer to production.

The following topics introduce each MLflow component and describe how they are integrated with Azure Databricks. They include caveats about working with MLflow in the Azure Databricks environment and numerous Azure Databricks notebooks and examples that illustrate how to use each MLflow component.