Amazon SageMaker Data Wrangler helps you understand, aggregate, transform, and prepare data for machine learning (ML) from a single visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code.
Data science practitioners generate, observe, and process data to solve business problems where they need to transform and extract features from datasets. Transforms such as ordinal encoding or one-hot encoding learn encodings on your dataset. These encoded outputs are referred as trained parameters. As datasets change over time, it may be necessary to refit encodings on previously unseen data to keep the transformation flow relevant to your data.
We are excited to announce the refit trained parameter feature, which allows you to use previous trained parameters and refit them as desired. In this post, we demonstrate how to use this feature.
Overview of the Data Wrangler refit

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