Summary
In this chapter, we discussed the different stages in the ML life cycle. We picked a problem statement, performed data exploration, plotted a few graphs, did feature engineering and customer segmentation, and built a customer lifetime value model. We looked at the oversights and discussed the most time-consuming stages of ML. I wanted to get you onto the same page as I am and set a good foundation for the rest of this book.
In the next chapter, we will set the stage for the need for a feature store and how it could improve the ML process. We will also discuss the need to bring features into production and some of the traditional ways of doing so.
Chapter 1: An Overview of the Machine Learning Life Cycle
Chapter 1: An Overview of the Machine Learning Life Cycle