Machine Learning Engineering in Action

10 Standards of coding and creating maintainable ML code

This chapter covers

• Identifying ML code smells and how to correct them
• Reducing code complexity in ML projects
• Currying for cleaner and more understandable code
• Applying proper exception handling in ML code bases
• Understanding side effects and how they can create bugs
• Simplifying nested logic to improve comprehension

In the preceding chapter, we covered the broad strokes of a code foundation. Focusing on breaking up complex structure by utilizing refactoring and basic software-engineering best practices was important to pave the way for further discussion of the more detailed aspects of software development for ML. Without laying the foundation of basic best-practices, the code architecture ...