In this post, we walk you through two sampling techniques in Amazon SageMaker Data Wrangler so you can quickly create processing workflows for your data. We cover both random sampling and stratified sampling techniques to help you sample your data based on your specific requirements.
Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. You can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization, from a single visual interface. With Data Wrangler’s data selection tool, you can choose the data you want from various data sources and import it with a single click. Data Wrangler contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. With Data Wrangler’s visualization templates, you

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