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Amazon Personalize is integrating with Amazon SageMaker Data Wrangler to make it easier for customers to import and prepare their data. Amazon Personalize enables developers to improve customer engagement through personalized product and content recommendations – no ML expertise required. The quality of data used for model training affects the quality of the recommendations, which makes data aggregation and preparation a critical step to get high-quality recommendations using Amazon Personalize. With this launch, Amazon Personalize gives you the ability to prepare your data through Amazon SageMaker Data Wrangler before using it in Amazon Personalize. Customers can use Amazon SageMaker Data Wrangler to import data from 40+ supported data sources and perform end-to-end data preparation (including data selection, cleansing, exploration, visualization, and processing at scale) in a single user interface using little to no code. This allows customers to rapidly prepare their users, items or interactions dataset using Amazon SageMaker Data Wrangler by leveraging over 300 built-in data transformations, retrieving data insights, and quickly iterating by fixing data issues. View the full article

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, Databricks Delta Lake, and Snowflake. View the full article