Databricks Announces MLflow 2.0 to Deliver Powerful Tools for MLOps

Databricks is releasing MLflow 2.0, building upon MLflow's strong platform foundation and incorporating extensive user feedback to simplify data science workflows and deliver innovative, first-class tools for MLOps.

Features and improvements include extensions to MLflow Recipes (formerly MLflow Pipelines) such as AutoML, hyperparameter tuning, and classification support, as well modernized integrations with the ML ecosystem, a streamlined MLflow Tracking UI, a refresh of core APIs across MLflow's platform components, and much more.

MLflow Recipes enables data scientists to rapidly develop high-quality models and deploy them to production. With MLflow Recipes, users can get started quickly using predefined solution recipes for a variety of ML modeling tasks, iterate faster with the Recipes execution engine, and easily ship robust models to production by delivering modular, reviewable model code and configurations without any refactoring.

MLflow 2.0 incorporates MLflow Recipes as a core platform component. It also makes several significant extensions, including support for classification models, improved data profiling, and hyperparameter tuning capabilities.

In MLflow 2.0, the company is introducing a refresh of core platform APIs and the MLflow Tracking UI based on extensive feedback from MLflow users and Databricks customers. The simplified platform experience streamlines your data science and MLOps workflows, helping you reach production faster.

The refreshed MLflow experiment page distills the most relevant model performance information and enables users to pin the best runs for future reference as experimentation progresses. In MLflow 2.0, every run has a unique name for easy identification and tracking.

MLflow 2.0 also includes a revamped integration with TensorFlow and Keras, unifying logging and scoring functionalities for both model types behind a common interface.

Managed MLflow 2.0 is part of the Databricks platform for end-to-end production machine learning built on the open lakehouse architecture, which includes Feature Store and Serverless Real-time Inference.

For more information about this release, visit