![]() After your data is prepared, you can set up SageMaker data processing jobs train, tune, and deploy models using SageMaker Autopilot or deploy your data preparation flow for inference, all from the SageMaker Data Wrangler UI.ĭescription text: A faster, visual way to aggregate and prepare data for MLĭescription text: Select and query data from a variety of data sources such as Amazon S3, Athena, Amazon EMR, Amazon Redshift, Snowflake, Databricks, and 40+ other third-party sources SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly transform data without writing any code. ![]() ![]() Next, you can use the Data Quality and Insights report to automatically verify data quality and detect anomalies, such as duplicate rows and target leakage. You can use SQL to select the data you want from a wide variety of data sources and import it quickly. With SageMaker Data Wrangler, 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. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes.
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