We are happy to announce the availability of MLflow 0.9.0!

MLflow 0.9.0 introduces several major features:

  • Support for running MLflow Projects in Docker containers.
  • Database stores for the MLflow Tracking Server.
  • Simplified custom Python model packaging.
  • Plugin systems allowing third party libraries to extend MLflow functionality.
  • Support for HTTP authentication to the Tracking Server in the R client.

And a few breaking changes:

  • [Scoring] The pyfunc scoring server now expects requests with the application/json content type to contain json-serialized pandas dataframes in the split format, rather than the records format. Also, when reading the pandas dataframes from JSON, the scoring server no longer automatically infers data types as it can result in unintentional conversion of data types.
  • [API] Removed GetMetric & GetParam from the REST API as they are subsumed by GetRun.

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