Machine Learning Engineering in Action

14 Writing production code

This chapter covers

• Validating feature data before attempting to use it for a model
• Monitoring features in production
• Monitoring all aspects of a production model life cycle
• Approaching projects with the goal of solving them in the simplest manner possible
• Defining a standard code architecture for ML projects
• Avoiding cargo cult behavior in ML

We spent the entirety of part 2 of this book on the more technician-focused aspects of building ML software. In this chapter, we’ll begin the journey of looking at ML project work from the eyes of an architect.

We’ll focus on the theory and philosophy of approaches to solving problems with ML from the highly interconnected, intensely complex, and altogether holistic view of ...