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Amazon AWS launches Redshift ML to let developers train models with SQL

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2021-05-28 06:32:11

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Amazon today announced the general availability of Redshift ML, which lets customers use SQL to query and combine structured and semi-structured data across data warehouses, operational databases, and data lakes. The company says that Redshift ML can be used to create, train, and deploy machine learning models directly from an Amazon Redshift instance.

In the past, Amazon Web Services (AWS) customers who wanted to process data from Amazon Redshift to train an AI model would have to export the data to an Amazon Simple Storage Service (Amazon S3) bucket and configure and start training. This required many different skills and usually more than one person to complete, raising the barrier to entry for enterprises looking to forecast revenue, predict customer churn, detect anomalies, and more.

With Redshift ML, customers can create a model using an SQL query to specify training data and the output value they want to predict. For example, to create a model that predicts the success rate of marketing activities, a customer might define their inputs by selecting database columns that include customer profiles and results from previous marketing campaigns. After running an SQL command, Redshift ML exports the data from Amazon Redshift to an S3 bucket and calls Amazon SageMaker Autopilot to prepare the data, select an algorithm, and apply the algorithm for model training. Customers can optionally select the algorithm to use if they opt not to defer to SageMaker Autopilot.