Chapter 7: Building the ML Workflow Using AWS Step Functions

In this chapter, we will continue from where we left off in Chapter 6, Automating the Machine Learning Process Using AWS Step Functions. You will recall from that chapter that the primary goal we are working toward achieving is to streamline the process gap that was originally highlighted in Chapter 4, Continuous Integration and Continuous Delivery (CI/CD) for Machine Learning—namely, to automate the handover of trained machine learning (ML) models from the ML practitioner teams to the development teams. Since we've already created continuous integration/continuous delivery (CI/CD) pipeline artifacts, as the application development engineers, the next step to achieving our goal is to provide the ML practitioner's contribution to further automate the end-to-end (E2E) process.

So, in this chapter, we are going to create a processing process that creates training and testing datasets, trains an ML model,...