We are happy to announce the availability of MLflow 0.6.0!

MLflow 0.6.0 introduces several major features:

  • A Java client API (to be published on Maven within the next day or two)
  • Support for saving and serving SparkML models as MLeap for low-latency serving
  • Support for tagging runs with metadata, during and after the run completion
  • Support for deleting (and restoring deleted) experiments

In addition to these features, there are a host of improvements and bugfixes to the REST API, Python API, tracking UI, and documentation. Visit the change log to read more about the new features.