Chapter 9: Building the ML Workflow Using Amazon Managed Workflows for Apache Airflow

In previous iterations of the Age Calculator example, we learned how applying a source code-centric methodology for ML workflow automation has been accomplished through cross-functional collaboration between the ML practitioner and developer teams. In Chapter 8, Automating the Machine Learning Process Using Apache Airflow, we explained how data engineering teams can use Amazon's MWAA to create the platform where the ML practitioner can automate the ML workflow as an Airflow DAG.

So, to build a successful data-centric ML workflow, we need to apply the same methodology to create an agile, cross-functional collaboration between the ML practitioner and data engineering teams. Therefore, in this chapter, we are going to continue where we left off in Chapter 8, Automating the Machine Learning Process Using Apache Airflow. In the previous chapter, we used the AWS CDK to construct the MWAA prerequisites...