AWS – Amazon SageMaker Autopilot now includes Amazon SageMaker Data Wrangler feature transforms when deploying models for inference

Amazon SageMaker Data Wrangler reduces the time to aggregate and prepare data for machine learning (ML) from weeks to minutes. Amazon 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. Data Wrangler enables a unified data preparation and model training experience with Amazon SageMaker Autopilot in just a few clicks. This integration is now enhanced to include and reuse Data Wrangler feature transforms such as missing value imputers, ordinal/one-hot encoders etc., along with the Autopilot models for ML inference. When you prepare data in Data Wrangler and train a model by invoking Autopilot, you can now deploy the trained model along with all the Data Wrangler feature transforms as a SageMaker Serial Inference Pipeline. This will enable automatic preprocessing of the raw data with the reuse of Data Wrangler feature transforms at the time of inference. This feature is currently only supported for Data Wrangler flows that do not use join, group by, concatenate and time series transformations.

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