SageMaker Spark 1.0.4

• spark/pyspark feature: LinearLearnerEstimator: Add more hyper-parameters

SageMaker Spark 1.0.3

1.0.3

• feature: XGBoostSageMakerEstimator: Fix maxDepth hyperparameter to use correct type (Int)

SageMaker Spark 1.0.1

• feature: Estimators: add support for Amazon FactorizationMachines algorithm
• feature: Documentation: multiple updates to README, scala docs, addition of CHANGELOG.rst file
• feature: Setup: update SBT plugins
• feature: Setup: add travis file

SageMaker Spark 1.0.0

SageMaker Spark is an open source Spark library for Amazon SageMaker. With SageMaker Spark you construct Spark ML Pipelines using Amazon SageMaker stages. These pipelines interleave native Spark ML stages and stages that interact with SageMaker training and model hosting.

With SageMaker Spark, you can train on Amazon SageMaker from Spark DataFrames using Amazon-provided ML algorithms like K-Means clustering or XGBoost, and make predictions on DataFrames against SageMaker endpoints hosting your trained models, and, if you have your own ML algorithms built into SageMaker compatible Docker containers, you can use SageMaker Spark to train and infer on DataFrames with your own algorithms -- all at Spark scale.