We are happy to announce the availability of MLflow 0.8.1! MLflow 0.8.1 introduces several significant improvements:

  • Improved UI responsiveness and load time, especially when displaying experiments containing hundreds to thousands of runs.

  • Improved visualizations, including interactive scatter plots for MLflow run comparisons.

  • Expanded support for scoring Python models as Spark UDFs. For more information, see the updated documentation for this feature.

  • By default, saved models will now include a Conda environment specifying all of the dependencies necessary for loading them in a new environment.

  • MLflow projects can now be run from ZIP files.

The release includes additional bugfixes and improvements across the Python client, tracking UI, and documentation. Visit the change log to read more about the new features.