Summary

In this chapter, we discussed why Amazon Redshift ML is a good choice to use data in your data warehouse to make predictions.

By bringing ML to your data warehouse, Amazon Redshift ML enables you to greatly shorten the amount of time to create and train models by putting the power of ML directly in the hands of your developers, data analysts, and BI professionals.

Your data remains secure; it never leaves your VPC. Plus, you can easily control access to create and use models.

Finally, we showed you different methods of creating models in Redshift ML, such as using AUTO, how to guide model training, and an advanced method to supply hyperparameters.

Now, you understand how ML fits into your data warehouse, how to use proper security and configuration guidelines with Redshift ML, and how a model is trained in Amazon SageMaker.

In the next chapter, you will get hands-on and create your first model using Amazon Redshift ML, learn how to validate the model, and learn...