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.
- [API/CLI] Support for running MLflow projects from ZIP files (#759, @jmorefieldexpe)
- [Python API] Support for passing model conda environments as dictionaries to
log_modelfunctions (#748, @dbczumar)
- [Models] Default Anaconda environments have been added to many Python model flavors. By default, models produced by
log_modelfunctions will include an environment that specifies all of the versioned dependencies necessary to load and serve the models. Previously, users had to specify these environments manually. (#705, #707, #708, #749, @dbczumar)
- [Scoring] Support for synchronous deployment of models to SageMaker (#717, @dbczumar)
- [Tracking] Include the Git repository URL as a tag when tracking an MLflow run within a Git repository (#741, @whiletruelearn, @mateiz)
- [UI] Improved runs UI performance by using a react-virtualized table to optimize row rendering (#765, #762, #745, @smurching)
- [UI] Significant performance improvements for rendering run metrics, tags, and parameter information (#764, #747, @smurching)
- [UI] Scatter plots, including run comparsion plots, are now interactive (#737, @mateiz)
- [UI] Extended CSRF support by allowing the MLflow UI server to specify a set of expected headers that clients should set when making AJAX requests (#733, @aarondav)
Bug fixes and documentation updates:
- [Python/Scoring] MLflow Python models that produce Pandas DataFrames can now be evaluated as Spark UDFs correctly. Spark UDF outputs containing multiple columns of primitive types are now supported (#719, @tomasatdatabricks)
- [Scoring] Fixed a serialization error that prevented models served with Azure ML from returning Pandas DataFrames (#754, @dbczumar)
- [Docs] New example demonstrating how the MLflow REST API can be used to create experiments and log run information (#750, kjahan)
- [Docs] R documentation has been updated for clarity and style consistency (#683, @stbof)
- [Docs] Added clarification about user setup requirements for executing remote MLflow runs on Databricks (#736, @andyk)