I am quite surprised there is no discussion here. The book actually gives a nice overview of practical Software Engineering principles applied to ML Engineering and hence of use to regular Programmers moving to ML from other domains. I personally found it quite useful to understand the practices employed in ML Engineering and how it is different from "normal" programming which is where i come from.

Part II titled "Best Practices for Machine Learning Pipelines" and starting from chapter 5 is where the meat lies.