AWS announces general availability of Amazon Redshift ML
We're excited to announce that Redshift ML is now generally available. Amazon Redshift ML enables you to create, train, and deploy machine learning (ML) models using familiar SQL commands. With Amazon Redshift ML, you can now leverage Amazon SageMaker, a fully managed machine learning service, without moving your data or learning new skills.
We're excited to announce that Redshift ML is now generally available. Amazon Redshift ML enables you to create, train, and deploy machine learning (ML) models using familiar SQL commands. With Amazon Redshift ML, you can now leverage Amazon SageMaker, a fully managed machine learning service, 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 the best model and tunes it based on training data using Amazon SageMaker Autopilot. SageMaker Autopilot chooses between regression, binary, or multi-class classification models. Alternatively, you can choose a specific model type such as Xtreme Gradient Boosted tree (XGBoost) or multilayer perceptron (MLP), 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. You can also import your pre-trained SageMaker Autopilot, XGBoost or MLP models into your Amazon Redshift cluster for local inference.
Amazon Redshift ML also provides you the ability to invoke custom ML models deployed in remote SageMaker endpoints.
Amazon Redshift ML leverages your existing cluster resources for prediction so you can avoid additional Amazon Redshift charges. When you create a model in Amazon Redshift, Amazon Redshift ML uses Amazon SageMaker for training your model. You pay only the associated SageMaker costs. There is no additional Amazon Redshift charge for creating or using a model, and prediction happens locally in your Amazon Redshift cluster. View the Redshift pricing page for details.
The Redshift ML 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 (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 Amazon Redshift documentation or read this blog post.