We've explored building the machine learning models from scratch, we've learned about tuning a classifier, and we've understood the usage of a third-party library, all in a test-driven manner. We haven't really talked about building a full system around these techniques. When we say "full system", we're talking about something that promotes sustainable and controlled software development. If we build an entire machine learning project and aren't careful about it, we can end up being tightly coupled with a third-party library. This is a bad thing because this could put us in a situation where changing the underlying library could mean we have to rewrite the entire system.

Why is this important? After we create our first model that is successful what are our options for further improvement down the road? If we chose a great library then we'll have many options for tuning our model and probably even many options for other models. Do we really want...