Posted On: Jun 9, 2022

Today, you can export features into Amazon SageMaker Feature Store faster than ever with export functionality now available in Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. 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. With SageMaker Data Wrangler’s data selection tool, you can quickly select data from multiple data sources, such as Amazon S3, Amazon Athena, Amazon Redshift, AWS Lake Formation, Amazon SageMaker Feature Store, and Snowflake. Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, retrieve, and share machine learning (ML) features.

Starting today you can create and export features to Amazon SageMaker Feature Store in just a few clicks with Amazon SageMaker Data Wrangler. Previously, engineering features and exporting them into a feature store when preparing data for machine learning would require writing a substantial amount of code. You can now engineer your features using SageMaker Data Wrangler’s visual point-and-click interface and export features to SageMaker Feature Store in just a few clicks. You can also now easily browse feature groups, create new feature groups, and validate feature group schemas all from within SageMaker Data Wrangler.

To get started with new capabilities of Amazon SageMaker Data Wrangler, you can open Amazon SageMaker Studio after upgrading to the latest release and click File > New > Flow from the menu or “new data flow” from the SageMaker Studio launcher. To learn more about the new features, read the blog and view the documentation.