Summary
In this chapter, we reviewed important concepts and approaches for debugging in software development and their differences with machine learning model debugging. You learned that debugging in machine learning modeling is beyond software debugging and how data and algorithms, in addition to code, could cause flawed or low-performance models and unreliable predictions. You can benefit from these understandings and the tools and techniques you will learn about throughout this book to develop reliable machine learning models.
In the next chapter, you will learn about the different components of the machine learning life cycle. You will also learn how modularizing machine learning modeling with these components helps us in identifying opportunities for improving our models before and after training and testing.
