Releases · mlflow/mlflow

Breaking changes:

[MLflow Server] Renamed --artifact-root parameter to --default-artifact-root in mlflow server to better reflect its purpose (#165, @aarondav)

Features:

Spark MLlib integration: we now support logging SparkML Models directly in the log_model API, model format, and serving APIs (#72, @tomasatdatabricks)
Google Cloud Storage is now supported as an artifact storage root (#152, @bnekolny)
Support asychronous/parallel execution of MLflow runs (#82, @smurching)
[SageMaker] Support for deleting, updating applications deployed via SageMaker (#145, @dbczumar)
[SageMaker] Pushing the MLflow SageMaker container now includes the MLflow version that it was published with (#124, @sueann)
[SageMaker] Simplify parameters to SageMaker deploy by providing sane defaults (#126, @sueann)
[UI] One-element metrics are now displayed as a bar char (#118, @cryptexis)

Bug fixes:

Require gitpython>=2.1.0 (#98, @aarondav)
Fixed TensorFlow model loading so that columns match the output names of the exported model (#94, @smurching)
Fix SparkUDF when number of columns >= 10 (#97, @aarondav)
Miscellaneous bug and documentation fixes from @emres, @dmatrix, @stbof, @gsganden, @dennyglee, @anabranch, @mikehuston, @andrewmchen, @juntai-zheng

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