Summary

In this chapter, we continued to build on the CDK application we started in Chapter 4, Continuous Integration and Continuous Delivery (CI/CD) for Machine Learning. In doing so, you were further presented with how to deploy the CDK application and automate the deployment of an optimized ML model.

You were also introduced to the importance of an agile, cross-function team as being integral to the success of an automated ML solution. We saw how these various teams bridged the gap between the ML modeling process (from the perspective of ML practitioners), all the way to automated model deployment (from the perspective of application development and operations teams).

Additionally, in this chapter, you saw how the AWS development tools, namely CodeCommit, CodeBuild, and CodePipeline, can be used to orchestrate the CI/CD process. Though the hands-on example, you saw for yourself how the typical ML process introduced in Chapter 1, Getting Started with Automated Machine Learning...