On the Amazon Web Services (AWS) Cloud, you can use Amazon Redshift machine learning (Amazon Redshift ML) to perform ML analytics on data stored in either an Amazon Redshift cluster or on Amazon Simple Storage Service (Amazon S3). Amazon Redshift ML supports supervised learning, which is typically used for advanced analytics. Use cases for Amazon Redshift ML include revenue forecasting, credit card fraud detection, and customer lifetime value (CLV) or customer churn predictions.
Amazon Redshift ML makes it easy for database users to create, train, and deploy ML models by using standard SQL commands. Amazon Redshift ML uses Amazon SageMaker Autopilot to automatically train and tune the best ML models for classification or regression based on your data, while you retain control and visibility.
All interactions between Amazon Redshift, Amazon S3, and Amazon SageMaker are abstracted away and automated. After the ML model is trained and deployed, it becomes available as a user-defined function (UDF) in Amazon Redshift and can be used in SQL queries.
This pattern complements the Create, train, and deploy ML models in Amazon Redshift using SQL with Amazon Redshift ML from the AWS Blog, and the Build, train, and deploy an ML model with Amazon SageMaker tutorial from the Getting Started Resource Center.
