To ensure your sample is a good representation of overall population, you can employ sampling strategies. Sampling offers a practical solution that sacrifices some accuracy for the sake of practicality and ease. Statistical sampling is a procedure that allows you to understand your data by selecting subsets from the population. When working with data, it’s often not computationally feasible to measure every observation from the population. In statistical analysis, the total set of observations is known as the population. After your data is prepared, you can build fully automated ML workflows with Amazon SageMaker Pipelines and save them for reuse in Amazon SageMaker Feature Store. With Data Wrangler’s visualization templates, you can quickly preview and inspect that these transformations are completed as you intended by viewing them in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. 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 data selection tool, you can choose the data you want from various data sources and import it with a single click. 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. We cover both random sampling and stratified sampling techniques to help you sample your data based on your specific requirements.ĭata Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes.
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.
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