AWS announces Amazon Redshift ML (preview)
Amazon Redshift ML makes it possible for data warehouse users such as data analysts, database developers, and data scientists to create, train, and deploy machine learning (ML) models using familiar SQL commands. Amazon Redshift is the most widely used cloud data warehouse and, with Amazon Redshift ML, you can now leverage Amazon SageMaker, a fully managed machine learning service, using SQL and without moving your data or learning new skills.
Amazon Redshift ML makes it possible for data warehouse users such as data analysts, database developers, and data scientists to create, train, and deploy machine learning (ML) models using familiar SQL commands. Amazon Redshift is the most widely used cloud data warehouse and, with Amazon Redshift ML, you can now leverage Amazon SageMaker, a fully managed machine learning service, using SQL and without moving your data or learning new skills.
With Amazon Redshift ML powered by Amazon SageMaker, you can use SQL statements to create and train machine learning models from your data in Amazon Redshift and then use these models for use cases such as churn prediction and fraud risk scoring directly in your queries and reports. Amazon Redshift ML automatically discovers and tunes the best model based on the training data using Amazon SageMaker Autopilot. SageMaker Autopilot chooses among the best regression, binary, or multi-class classification and linear models.
Alternatively, you can choose a model type such as Xtreme Gradient Boosted tree (XGBoost), a problem type like regression or classification, and preprocessors or hyperparameters. Amazon Redshift ML uses your parameters to build, train, and deploy the model in the Amazon Redshift data warehouse. You can obtain predictions from these trained models using SQL queries as if you were invoking a user defined function (UDF) and leverage all benefits of Amazon Redshift, including massively parallel processing capabilities.
Amazon Redshift ML leverages your existing cluster resources for prediction so you can avoid additional Amazon Redshift charges. There is no additional Amazon Redshift charge for creating or using a model, and prediction happens locally in your Amazon Redshift cluster, so you don’t have to pay extra unless you need to resize your cluster. Amazon Redshift ML uses Amazon SageMaker for training your model, which does have an additional associated cost. View the Redshift pricing page for details.
The Redshift ML preview is available in the following regions: US East (Ohio), US East (N Virginia), US West (Oregon), US West (San Francisco), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe(London), Europe (Paris), Europe (Stockholm), Asia Pacific (Hong Kong) Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney), and South America (São Paulo). To get started and learn more, visit the preview documentation or read this blog post.