Amazon SageMaker Autopilot now provides custom data split options along with an improved experience for creating an AutoML experiment

SageMaker Autopilot automatically builds, trains and tunes the best machine learning models based on your data, while allowing you to maintain full control and visibility. Starting today, when creating Autopilot experiment to train a machine learning model, you can customize the splits of data used for training and validation of models. By default Autopilot splits the specified dataset into 80-20 percent splits reserved for training and validation respectively. With this release, you can customize the training and validation data split percentages or alternatively provide two datasets, one for training and another for validation. This feature is available for use in both Amazon SageMaker Studio and SageMaker Autopilot API.

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