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MLflow 1.11.0

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@smurching smurching released this 31 Aug 07:33
· 1934 commits to master since this release

We are happy to announce the availability of MLflow 1.11.0!

In addition to bug and documentation fixes, MLflow 1.11.0 includes the following features and improvements:

  • New mlflow.sklearn.autolog() API for automatic logging of metrics, params, and models from scikit-learn model training (#3287, @harupy; #3323, #3358 @dbczumar)
  • Registered model & model version creation APIs now support specifying an initial description (#3271, @sueann)
  • The R mlflow_log_model and mlflow_load_model APIs now support XGBoost models (#3085, @lorenzwalthert)
  • New mlflow.list_run_infos fluent API for listing run metadata (#3183, @trangevi)
  • Added section for visualizing and comparing model schemas to model version and model-version-comparison UIs (#3209, @zhidongqu-db)
  • Enhanced support for using the model registry across Databricks workspaces: support for registering models to a Databricks workspace from outside the workspace (#3119, @sueann), tracking run-lineage of these models (#3128, #3164, @ankitmathur-db; #3187, @harupy), and calling mlflow.<flavor>.load_model against remote Databricks model registries (#3330, @sueann)
  • UI support for setting/deleting registered model and model version tags (#3187, @harupy)
  • UI support for archiving existing staging/production versions of a model when transitioning a new model version to staging/production (#3134, @harupy)

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