Depending on the quality and complexity of data, data scientists spend between 45–80% of their time on data preparation tasks. This implies that data preparation and cleansing take valuable time away from real data science work. After a machine learning (ML) model is trained with prepared data and readied for deployment, data scientists must often rewrite the data transformations used for preparing data for ML inference. This may stretch the time it takes to deploy a useful model that can inference and score the data from its raw shape and form.
In Part 1 of this series, we demonstrated how Data Wrangler enables a unified data preparation and model training experience with Amazon SageMaker Autopilot in just a few clicks. In this second and final part of this series, we focus on a feature that includes and reuses Amazon SageMaker Data Wrangler transforms, such as missing value imputers, ordinal or

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