Deploying the CI/CD pipeline

You will recall from Chapter 4, Continuous Integration and Continuous Delivery (CI/CD) for Machine Learning, that we concluded the chapter by checkpointing the intrinsic artifacts, namely the buld.py and deploy.py scripts, and committing them into the CodeCommit repository. Whereas these artifacts fundamentally create and deploy a trained ML model, we still need to wrap them in a continuous integration and continuous deployment process. To accomplish this, we will continue using the AWS CDK to create a codified CI/CD pipeline construct.

Codifying the pipeline construct

The penultimate component of the application is the pipeline construct itself. Using the following steps, we will once again leverage the AWS CDK to create the pipeline:

1. If you don't already have the Cloud9 environment open in your web browser, log into the AWS account you've been using, and open the Cloud9 management console (https://console.aws.amazon.com/cloud9...