Over the course of this chapter, we worked iteratively on improving the machine learning model we built in Chapter 13, Training a Machine Learning Model—adding features and tuning it to achieve maximum performance. As the code and iterations get more complex and multiple trial-and-error attempts are required, it is important to keep track of your research. Therefore, we further discussed how to keep track of not only the code but also data and metrics, making sure we can always switch back and reproduce any of the previous versions. 

In the next chapter, we'll take another stab at our Wikipedia scraping code, building it into an independent Python library you could share with your friends and colleagues. Throughout the rest of this book, we will focus on different ways of delivering our code as a product to the client—as a standalone package, scheduled data pipeline, online dashboard, or API endpoint.