We are happy to announce the availability of MLflow 2.0.1!

The 2.0.1 version of MLflow is a major milestone release that focuses on simplifying the management of end-to-end MLOps workflows, providing new feature-rich functionality, and expanding upon the production-ready MLOps capabilities offered by MLflow. Check out the MLflow 2.0 blog post for an in-depth walk through!

This release contains several important breaking changes from the 1.x API, additional major features and improvements.

Features:

• [Recipes] MLflow Pipelines is now MLflow Recipes - a framework that enables data scientists to quickly develop high-quality models and deploy them to production
• [Recipes] Add support for classification models to MLflow Recipes (#7082, @bbarnes52)
• [UI] Introduce support for pinning runs within the experiments UI (#7177, @harupy)
• [UI] Simplify the layout and provide customized displays of metrics, parameters, and tags within the experiments UI (#7177, @harupy)
• [UI] Simplify run filtering and ordering of runs within the experiments UI (#7177, @harupy)
• [Tracking] Update mlflow.pyfunc.get_model_dependencies() to download all referenced requirements files for specified models (#6733, @harupy)
• [Tracking] Add support for selecting the Keras model save_format used by mlflow.tensorflow.autolog() (#7123, @balvisio)
• [Models] Set mlflow.evaluate() status to stable as it is now a production-ready API
• [Models] Simplify APIs for specifying custom metrics and custom artifacts during model evaluation with mlflow.evaluate() (#7142, @harupy)
• [Models] Correctly infer the positive label for binary classification within mlflow.evaluate() (#7149, @dbczumar)
• [Models] Enable automated signature logging for tensorflow and keras models when mlflow.tensorflow.autolog() is enabled (#6678, @BenWilson2)
• [Models] Add support for native Keras and Tensorflow Core models within mlflow.tensorflow (#6530, @WeichenXu123)
• [Models] Add support for defining the model_format used by mlflow.xgboost.save/log_model() (#7068, @AvikantSrivastava)
• [Scoring] Overhaul the model scoring REST API to introduce format indicators for inputs and support multiple output fields (#6575, @tomasatdatabricks; #7254, @adriangonz)
• [Scoring] Add support for ragged arrays in model signatures (#7135, @trangevi)
• [Java] Add getModelVersion API to the java client (#6955, @wgottschalk)

Breaking Changes:

The following list of breaking changes are arranged by their order of significance within each category.

• [Core] Support for Python 3.7 has been dropped. MLflow now requires Python >=3.8
• [Recipes] mlflow.pipelines APIs have been replaced with mlflow.recipes
• [Tracking / Registry] Remove /preview routes for Tracking and Model Registry REST APIs (#6667, @harupy)
• [Tracking] Remove deprecated list APIs for experiments, models, and runs from Python, Java, R, and REST APIs (#6785, #6786, #6787, #6788, #6800, #6868, @dbczumar)
• [Tracking] Remove deprecated runs response field from Get Experiment REST API response (#6541, #6524 @dbczumar)
• [Tracking] Remove deprecated MlflowClient.download_artifacts API (#6537, @WeichenXu123)
• [Tracking] Change the behavior of environment variable handling for MLFLOW_EXPERIMENT_NAME such that the value is always used when creating an experiment (#6674, @BenWilson2)
• [Tracking] Update mlflow server to run in --serve-artifacts mode by default (#6502, @harupy)
• [Tracking] Update Experiment ID generation for the Filestore backend to enable threadsafe concurrency (#7070, @BenWilson2)
• [Tracking] Remove dataset_name and on_data_{name | hash} suffixes from mlflow.evaluate() metric keys (#7042, @harupy)
• [Models / Scoring / Projects] Change default environment manager to virtualenv instead of conda for model inference and project execution (#6459, #6489 @harupy)
• [Models] Move Keras model logging APIs to the mlflow.tensorflow flavor and drop support for TensorFlow Estimators (#6530, @WeichenXu123)
• [Models] Remove deprecated mlflow.sklearn.eval_and_log_metrics() API in favor of mlflow.evaluate() API (#6520, @dbczumar)
• [Models] Require mlflow.evaluate() model inputs to be specified as URIs (#6670, @harupy)
• [Models] Drop support for returning custom metrics and artifacts from the same function when using mlflow.evaluate(), in favor of custom_artifacts (#7142, @harupy)
• [Models] Extend PyFuncModel spec to support conda and virtualenv subfields (#6684, @harupy)
• [Scoring] Remove support for defining input formats using the Content-Type header (#6575, @tomasatdatabricks; #7254, @adriangonz)
• [Scoring] Replace the --no-conda CLI option argument for native serving with --env-manager='local' (#6501, @harupy)
• [Scoring] Remove public APIs for mlflow.sagemaker.deploy() and mlflow.sagemaker.delete() in favor of MLflow deployments APIs, such as mlflow deployments -t sagemaker (#6650, @dbczumar)
• [Scoring] Rename input argument df to inputs in mlflow.deployments.predict() method (#6681, @BenWilson2)
• [Projects] Replace the use_conda argument with the env_manager argument within the run CLI command for MLflow Projects (#6654, @harupy)
• [Projects] Modify the MLflow Projects docker image build options by renaming --skip-image-build to --build-image with a default of False (#7011, @harupy)
• [Integrations/Azure] Remove deprecated mlflow.azureml modules from MLflow in favor of the azure-mlflow deployment plugin (#6691, @BenWilson2)
• [R] Remove conda integration with the R client (#6638, @harupy)

Bug fixes:

• [Recipes] Fix rendering issue with profile cards polyfill (#7154, @hubertzub-db)
• [Tracking] Set the MLflow Run name correctly when specified as part of the tags argument to mlflow.start_run() (#7228, @Cokral)
• [Tracking] Fix an issue with conflicting MLflow Run name assignment if the mlflow.runName tag is set (#7138, @harupy)
• [Scoring] Fix incorrect payload constructor error in SageMaker deployment client predict() API (#7193, @dbczumar)
• [Scoring] Fix an issue where DataCaptureConfig information was not preserved when updating a Sagemaker deployment (#7281, @harupy)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.