v0.13

New Functionality:

• Export trained LightGBM models for evaluation outside of Spark

• LightGBM on Spark supports multiple cores per executor

• CNTKModel works with multi-input multi-output models of any CNTK
  datatype

• Added Minibatching and Flattening transformers for adding flexible
  batching logic to pipelines, deep networks, and web clients.

• Added Benchmark test API for tracking model performance across
  versions

• Added PartitionConsolidator function for aggregating streaming data
  onto one partition per executor (for use with connection/rate-limited
  HTTP services)

Updates and Improvements:

• Updated to Spark 2.3.0

• Added Databricks notebook tests to build system

• CNTKModel uses significantly less memory

• Simplified example notebooks

• Simplified APIs for MMLSpark Serving

• Simplified APIs for CNTK on Spark

• LightGBM stability improvements

• ComputeModelStatistics stability improvements

Acknowledgements:

We would like to acknowledge the external contributors who helped create
this version of MMLSpark (in order of commit history):

• 严伟, @terrytangyuan, @ywskycn, @dvanasseldonk, Jilong Liao,
  @chappers, @ekaterina-sereda-rf