Introduction

In the previous chapters, we covered the main concepts of machine learning, beginning with the distinction between the two main learning approaches (supervised and unsupervised learning), and then moved on to the specifics of some of the most popular algorithms in the data science community.

This chapter will talk about the importance of building complete machine learning programs, rather than just training models. This will involve taking the models to the next level, where they can be accessed and used easily.

We will do this by learning how to save a trained model. This will allow the best performing model to be loaded in order to make predictions over unseen data. We will also learn the importance of making a saved model available through platforms where users can easily interact with it.

This is especially important when working in a team, either for a company or for research purposes, as it allows all members of the team to use the model without needing...