Amazon Redshift, a fast, fully managed, widely used cloud data warehouse, natively integrates with Amazon SageMaker for machine learning (ML). Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Data analysts and database developers want to use this data to train ML models, which can then be used to generate insights for use cases such as forecasting revenue, predicting customer churn, and detecting anomalies.
Amazon Redshift ML makes it easy for SQL users to create, train, and deploy ML models using familiar SQL commands. In a previous post, we covered how Amazon Redshift ML allows you to use your data in Amazon Redshift with SageMaker, a fully managed ML service, without requiring you to become an expert in ML. We also discussed how Amazon Redshift ML enables ML experts to create XGBoost or MLP models in an earlier post.

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