Using built-in ML UDFs
In the previous section, we learned how we can create UDFs using Lambda. In this section, we're going to learn how to use Athena's built-in functionality to create UDFs that delegate down to a ML model. We're not going to delve too deeply into the ML aspects of things, though we will cover some basics just so you know what's happening. If you read Chapter 7, Ad Hoc Analytics, then some of this should be familiar.
Before you get started, note that you may incur some SageMaker charges during this. Particularly for the portion where we are training our models, we don't want to be waiting around forever, so we are leveraging the recommended cost/power instance type of ml.c5.xlarge. Total charges should be no more than a few dollars.
Pre-setup requirements
Before we are ready to head on over to SageMaker, there's a couple of things we need to put in place. First up is our favorite resource, an IAM role. By now, you're probably...