Executing the build
Execution of the build, in this case, will be very much about how we take the Proof-Of-Concept code shown in Chapter 1, Introduction to ML Engineering, and then split this out into components that can be called by another scheduling tool such as Apache Airflow. This will provide a showcase of how we can apply the skills we learned in Chapter 4, Packaging Up.
In the next few sections, we will walk through how to inject some engineering best practices into the code base, and we will discuss some coding examples to help bring this to reality. We will not focus on the scheduling and pipelining aspect for Apache Airflow (please refer to Chapter 5, Deployment Patterns and Tools, for this) but will focus instead on how some simple adaptations to an existing code base can dramatically improve its production readiness.
Not reinventing the wheel in practice
As discussed in Chapter 3, From Model to Model Factory, whether we run our ML pipeline in a train-run or train...
