Amazon Redshift | Redshift ML - Amazon Web Services
Amazon Redshift ML makes it easy for data analysts and database developers to create, train, and apply machine learning models using familiar SQL commands in Amazon Redshift data warehouses. With Redshift ML, you can take advantage of Amazon SageMaker, a fully managed machine learning service, without learning new tools or languages. Simply use SQL statements to create and train Amazon SageMaker machine learning models using your Redshift data and then use these models to make predictions. For example, you can use customer retention data in Redshift to train a churn detection model and then apply that model to your dashboards for your marketing team to offer incentives to customers at risk of churning. Redshift ML makes the model available as a SQL function within your Redshift data warehouse so you can easily apply it directly in your queries and reports.
No prior ML experience needed Because Redshift ML allows you to use standard SQL, it is easy for you to be productive with new use cases for your analytics data. Redshift ML provides simple, optimized, and secure integration between Redshift and Amazon SageMaker and enables inference within the Redshift cluster, making it easy to use predictions generated by ML-based models in queries and applications. There is no need to manage a separate inference model end point, and the training data is secured end-to-end with encryption.
