This is a guest post from Anum Rehman, Sr. Product Marketing Manager at Databricks.
MLflow, with over 13 million monthly downloads, has become the standard platform for end-to-end MLOps, enabling teams of all sizes to track, share, package and deploy any model for batch or real-time inference. On Databricks, Managed MLflow provides a managed version of MLflow with enterprise-grade reliability and security at scale, seamless integrations with the Databricks Machine Learning Runtime, Feature Store, and Serverless Real-Time Inference. Thousands of organizations are using MLflow on Databricks every day to power a wide variety of production machine learning applications.
Today, we are thrilled to announce the availability of MLflow 2.0. Building upon MLflow’s strong platform foundation, MLflow 2.0 incorporates 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, you 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, we are excited to introduce 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.
As you train and compare models, every MLflow Run you create now has a unique, memorable name to help you identify the best results. Later on, you can easily retrieve a group of MLflow runs by name or ID using expanded MLflow search filters, as well as search for experiments by name and by tags. When it comes time to deploy your models, MLflow 2.0’s revamped model scoring API offers a richer request and response format for incorporating additional information such as prediction confidence intervals.