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Found 2 results

  1. Amazon Web Services
    Today, we are making it faster and easier to prepare and visualize data using PySpark and Altair with support for code snippets 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, Databricks, and Snowflake. View the full article
  2. Amazon Web Services
    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. View the full article

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