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. Thousands of organizations are using MLflow every day to power a wide variety of production machine learning applications, and MLflow is actively developed by over 500 contributors across industry and academia.
Today, we are excited to announce the availability of MLflow 2.0! Building upon MLflow’s strong platform foundation, MLflow 2.0 incorporates extensive community 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 improved integrations with the ML ecosystem, a revamped MLflow Tracking UI, a refresh of core APIs across MLflow’s platform components, and much more.
MLflow Recipes enables data scientists to quickly build high-quality models and deploy them to production. With MLflow Recipes, you can get started easily using predefined solution recipes for a variety of ML modeling tasks, iterate faster with the Recipes execution engine, and ship robust models to production by delivering modular, reviewable model code and configurations without any refactoring. In MLflow 2.0, MLflow Recipes is now a core platform component with several new features, including support for classification models, improved data profiling and hyperparameter tuning capabilities.
MLflow Recipes automatically finds a high-quality model for your machine learning task using AutoML. Detailed performance insights and parameters are produced for further tuning and iteration.
MLflow 2.0 also adds AutoML to MLflow Recipes, dramatically reducing the amount of time required to produce a high-quality model. Simply specify a dataset and target column for your regression or classification task, and MLflow Recipes automatically explores a vast space of ML frameworks, architectures, and parameter configurations to build an optimal model. Model parameters are made readily available for further tuning, and comprehensive results are logged to MLflow Tracking for reproducible reference and comparison.
To get started with MLflow Recipes, watch the demo video and check out the quickstart guide on mlflow.org.
From day one, MLflow’s open interface design philosophy has simplified end-to-end machine learning workflows across all ML libraries and frameworks. With MLflow 2.0, we’re doubling down on our commitment to delivering first-class support for the latest and greatest tools in the machine learning ecosystem.
To this end, MLflow 2.0 includes a revamped integration with TensorFlow and Keras, unifying logging and scoring functionalities for both model types behind a common interface. The modernized mlflow.tensorflow module also offers a delightful experience for power users with TensorFlow Core APIs while maintaining simplicity for data scientists using Keras.
MLflow 2.0’s mlflow.evaluate() API creates rich model performance and explainability reports for any MLflow Model.
Additionally, in MLflow 2.0, the mlflow.evaluate() API for model evaluation is now stable and production-ready. With just a single line of code, mlflow.evaluate() creates a comprehensive performance report for any ML model. Simply specify a dataset and MLflow Model, and mlflow.evaluate() generates performance metrics, performance plots, and model explainability insights that are tailored to your modeling problem. You can also use mlflow.evaluate() to validate model performance against predefined thresholds and compare the performance of new models against a baseline, ensuring that your models meet production requirements. For more information about model evaluation, check out the "Model Evaluation in MLflow" blog post and the model evaluation documentation on mlflow.org.
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. 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. The upgraded MLflow Tracking Server in MLflow 2.0 also centralizes artifact management out of the box, making the platform much easier to run at scale.
The refreshed MLflow experiment page distills the most relevant model performance information and enables you to pin the best runs for future reference as your experimentation progresses. In MLflow 2.0, every run has a unique name for easy identification and tracking.
In addition to improving MLflow’s core APIs, we have redesigned the experiment page for MLflow Tracking, distilling the most relevant model information and simplifying the search experience. The new experiment page also includes a Run pinning feature for easily keeping track of the best models as your experiments progress.
We invite you to try out MLflow 2.0 today! To upgrade, simply install the MLflow library for your preferred programming language [Python][R][Java]. For a complete list of new features and improvements in MLflow 2.0, see the release changelog. For more information about how to get started with MLflow, visit the MLflow Quickstart Guide and the MLflow documentation.
MLflow 2.0 is just the beginning of the next generation MLOps platform. In the near future, we are excited to deliver additional improvements across the MLflow UI, including a brand new run comparison experience with improved visualizations, and more closely integrate data into the MLflow Tracking experience.
To learn more about upcoming developments in MLflow and contribute to the platform, check out the MLflow Roadmap. For first-time contributors, MLflow provides a curated list of good first issues. You can find more information about how to make your first contribution in the MLflow Contributing Guide. We are very excited to work with you to continue building the future of MLflow!