We are happy to announce the availability of MLflow 1.13.0!

In addition to bug and documentation fixes, MLflow 1.13.0 includes the following features and improvements:

New fluent APIs for logging in-memory objects as artifacts:​

• Add mlflow.log_text which logs text as an artifact (#3678, @harupy)
• Add mlflow.log_dict which logs a dictionary as an artifact (#3685, @harupy)
• Add mlflow.log_figure which logs a figure object as an artifact (#3707, @harupy)
• Add mlflow.log_image which logs an image object as an artifact (#3728, @harupy)

UI updates / fixes:​

• Add model version link in compact experiment table view
• Add logged/registered model links in experiment runs page view
• Enhance artifact viewer for MLflow models
• Model registry UI settings are now persisted across browser sessions
• Add model version description field to model version table

(#3867, @smurching)

Autologging enhancements:​

• Improve robustness of autologging integrations to exceptions (#3682, #3815, dbczumar; #3860, @mohamad-arabi; #3854, #3855, #3861, @harupy)
• Add disable configuration option for autologging (#3682, #3815, dbczumar; #3838, @mohamad-arabi; #3854, #3855, #3861, @harupy)
• Add exclusive configuration option for autologging (#3851, @apurva-koti; #3869, @dbczumar)
• Add log_models configuration option for autologging (#3663, @mohamad-arabi)
• Set tags on autologged runs for easy identification (and add tags to start_run) (#3847, @dbczumar)

More features and improvements:​

• Allow Keras models to be saved with SavedModel format (#3552, @skylarbpayne)
• Add support for statsmodels flavor (#3304, @olbapjose)
• Add support for nested-run in mlflow R client (#3765, @yitao-li)
• Deploying a model using mlflow.azureml.deploy now integrates better with the AzureML tracking/registry. (#3419, @trangevi)
• Update schema enforcement to handle integers with missing values (#3798, @tomasatdatabricks)

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