Building the ML model artifacts

Up to this point, we have focused on the various tasks that are typically performed by the application development teams, creating a CDK application for the overall structure of the automated process. In this section, we will continue this undertaking, but from the perspective of the ML practitioners, whereby we will create the ML model itself, as well as the artifacts responsible for executing the data processing, ML model training, and ML model evaluation processes. The following steps will show you how an ML practitioner might do this:

1. Using your AWS account, open the SageMaker console (https://console.aws.amazon.com/sagemaker/home).
2. Using the left-hand menu panel, click on the Studio option to open the SageMaker Domain dashboard.
3. In the SageMaker Domain dashboard, click on the Launch app drop-down menu and select Studio to launch the Studio UI in the browser.

   Note

   You should have a SageMaker Domain already configured in the SageMaker...