We are happy to announce the availability of MLflow 0.8.0! MLflow 0.8.0 introduces several major features:

  • Dramatically improved UI for comparing experiment run results:

    • Metrics and parameters are by default grouped into a single column, to avoid an explosion of mostly-empty columns. Individual metrics and parameters can be moved into their own column to help compare across rows.
    • Runs that are “nested” inside other runs (e.g., as part of a hyperparameter search or multistep workflow) now show up grouped by their parent run, and can be expanded or collapsed altogether. Runs can be nested by calling mlflow.start_run or mlflow.run while already within a run.
    • Run names (as opposed to automatically generated run UUIDs) now show up instead of the run ID, making comparing runs in graphs easier.
    • The state of the run results table, including filters, sorting, and expanded rows, is persisted in browser local storage, making it easier to go back and forth between an individual run view and the table.
  • Support for deploying models as Docker containers directly to Azure Machine Learning Service Workspace (as opposed to the previously-recommended solution of Azure ML Workbench).

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