We are happy to announce the availability of MLflow 1.24.0!

MLflow 1.24.0 includes several major features and improvements:


  • [Tracking] Support uploading, downloading, and listing artifacts through the MLflow server via mlflow server --serve-artifacts (#5320, @BenWilson2, @harupy)
  • [Tracking] Add the registered_model_name argument to mlflow.autolog() for automatic model registration during autologging (#5395, @WeichenXu123)
  • [UI] Improve and restructure the Compare Runs page. Additions include “show diff only” toggles and scrollable tables (#5306, @WeichenXu123)
  • [Models] Introduce mlflow.pmdarima flavor for pmdarima models (#5373, @BenWilson2)
  • [Models] When loading an MLflow Model, print a warning if a mismatch is detected between the current environment and the Model’s dependencies (#5368, @WeichenXu123)
  • [Models] Support computing custom scalar metrics during model evaluation with mlflow.evaluate() (#5389, @MarkYHZhang)
  • [Scoring] Add support for deploying and evaluating SageMaker models via the MLflow Deployments API <https://mlflow.org/docs/latest/models.html#deployment-to-custom-targets>_ (#4971, #5396, @jamestran201)

Bug fixes and documentation updates:

  • [Tracking / UI] Fix artifact listing and download failures that occurred when operating the MLflow server in --serve-artifacts mode (#5409, @dbczumar)
  • [Tracking] Support environment-variable-based authentication when making artifact requests to the MLflow server in --serve-artifacts mode (#5370, @TimNooren)
  • [Tracking] Fix bugs in hostname and path resolution when making artifacts requests to the MLflow server in --serve-artifacts mode (#5384, #5385, @mert-kirpici)
  • [Tracking] Fix an import error that occurred when mlflow.log_figure() was used without matplotlib.figure imported (#5406, @WeichenXu123)
  • [Tracking] Correctly log XGBoost metrics containing the @ symbol during autologging (#5403, @maxfriedrich)
  • [Tracking] Fix a SQL Server database error that occurred during Runs search (#5382, @dianacarvalho1)
  • [Tracking] When downloading artifacts from HDFS, store them in the user-specified destination directory (#5210, @DimaClaudiu)
  • [Tracking / Model Registry] Improve performance of large artifact and model downloads (#5359, @mehtayogita)
  • [Models] Fix fast.ai PyFunc inference behavior for models with 2D outputs (#5411, @santiagxf)
  • [Models] Record Spark model information to the active run when mlflow.spark.log_model() is called (#5355, @szczeles)
  • [Models] Restore onnxruntime execution providers when loading ONNX models with mlflow.pyfunc.load_model() (#5317, @ecm200)
  • [Projects] Increase Docker image push timeout when using Projects with Docker (#5363, @zanitete)
  • [Python] Fix a bug that prevented users from enabling DEBUG-level Python log outputs (#5362, @dbczumar)
  • [Docs] Add a developer guide explaining how to build custom plugins for mlflow.evaluate() (#5333, @WeichenXu123)

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