We are happy to announce the availability of MLflow 1.22.0!

MLflow 1.22.0 includes several major features and improvements:


  • [UI] Add a share button to the Experiment page (#4936, @marijncv)
  • [UI] Improve readability of column sorting dropdown on Experiment page (#5022, @WeichenXu123; #5018, @NieuweNils, @coder-freestyle)
  • [Tracking] Mark all autologging integrations as stable by removing @experimental decorators (#5028, @liangz1)
  • [Tracking] Add optional experiment_id parameter to mlflow.set_experiment() (#5012, @dbczumar)
  • [Tracking] Add support for XGBoost scikit-learn models to mlflow.xgboost.autolog() (#5078, @jwyyy)
  • [Tracking] Improve statsmodels autologging performance by removing unnecessary metrics (#4942, @WeichenXu123)
  • [Tracking] Update R client to tag nested runs with parent run ID (#4197, @yitao-li)
  • [Models] Support saving and loading all XGBoost model types (#4954, @jwyyy)
  • [Scoring] Support specifying AWS account and role when deploying models to SageMaker (#4923, @andresionek91)
  • [Scoring] Support serving MLflow models with MLServer (#4963, @adriangonz)

Bug fixes and documentation updates:

  • [UI] Fix bug causing Metric Plot page to crash when metric values are too large (#4947, @ianshan0915)
  • [UI] Fix bug causing parallel coordinate curves to vanish (#5087, @harupy)
  • [UI] Remove Creator field from Model Version page if user information is absent (#5089, @jinzhang21)
  • [UI] Fix model loading instructions for non-pyfunc models in Artifact Viewer (#5006, @harupy)
  • [Models] Fix a bug that added mlflow to conda.yaml even if a hashed version was already present (#5058, @maitre-matt)
  • [Docs] Add Python documentation for metric, parameter, and tag key / value length limits (#4991, @westford14)
  • [Examples] Update Python version used in Prophet example to fix installation errors (#5101, @BenWilson2)
  • [Examples] Fix Kubernetes resources specification in MLflow Projects + Kubernetes example (#4948, @jianyuan)

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