As with all ML projects, there is always room for improvement—especially if we converge on the actual use case scenario. But let's switch gears and talk about the technical side of the question.
As you probably noticed, in this chapter, we had to constantly iterate, adding and removing features from the data or settings to the model. And again, as we mentioned, only one-third of the initial experiments went into this book. This is probably fine for this toy dataset and this third of the code but eventually, we might be swamped in different versions and iterations of the model.
In Chapter 9, Shell, Git, Conda, and More – at Your Command, of this book, we learned about git—a system that stores versions of code, so you can safely switch to the previous version or even keep work on different versions of...
