MLflow v0.8.1 Features Faster Experiment UI and Enhanced Python Model

Added support to automatically capture dependencies in a Conda environment when saving models, ensuring that they can be loaded in a new environment Ability to run MLflow projects from ZIP files Faster and Improved MLflow UI Experience In our continued commitment to give ML developers an enjoyable experience, this release adds further enhancements to MLflow Experiment UI: Faster Display of Experiments: The improved MLflow UI can quickly display thousands of experiment runs, including all of their associated parameters and artifacts..Users who train large numbers of models should observe quicker response times..Better Visualizations With Interactive Scatter Plots: Scatter plots for comparing runs are now interactive, providing greater insight into model performance characteristics..Enhanced Python Model as Spark UDF When scoring Python models as Apache Spark UDFs, users can now filter UDF outputs by selecting from an expanded set of result types..For example, specifying a result type of pyspark.sql.types.DoubleType filters the UDF output and returns the first column that contains double precision scalar values..Specifying a result type of pyspark.sql.types.ArrayType(DoubleType) returns all columns that contain double precision scalar values..The example code below demonstrates result type selection using the result_type parameter..And the short example notebook illustrates Spark Model logged and then loaded as a Spark UDF..By default, pyfunc models produced by MLflow API calls such as save_model() and log_model() now include a Conda environment specifying all of the versioned dependencies necessary for loading them in a new environment..For example, the default Conda environment for the model trained in the example below has the following yaml representation: channels: – defaults dependencies: – python=3.5.2 – pyspark=2.4.0 name: mlflow-env Other Features and Bug Fixes In addition to these features, several other new pieces of functionality are included in this release..Some items worthy of note are: Features [API/CLI] Support for running MLflow projects from ZIP files (#759, @jmorefieldexpe) [Python API] Support for passing model conda environments as dictionaries to save_model and log_model functions (#748, @dbczumar) [Models] Default Anaconda environments have been added to many Python model flavors..By default, models produced by save_model and log_model functions will include an environment that specifies all of the versioned dependencies necessary to load and serve the models.. More details

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