Summary

In this chapter, we got a glimpse of what machine learning models look like from the inside, at least from the perspective of a programmer. This illustrated the major differences in how we construct machine learning-based software.

In classical models, we need to create a lot of pre-processing pipelines so that the model gets the right input. This means that we need to make sure that the data has the right properties and is in the right format; we need to work with the output to turn the predictions into something more useful.

In deep learning models, the data is pre-processed in a more streamlined way. The models can prepare the images and the text. Therefore, the software engineers’ task is to focus on the product and its use case rather than monitoring concept drift, data preparation, and post-processing.

In the next chapter, we’ll continue looking at examples of training machine learning models – both the classical ones and, most importantly...