As alluded to at the beginning of this chapter and seen firsthand in the previous section, working with machine learning models requires a set of new techniques and strategies to deal with uncertainty. The approach taken will be domain-specific, but there are some broad strategies that are worth keeping in mind, and that's what we will cover in this section in the context of the example project of this chapter.
The first is improving the model. Of course, there may be limitations depending on the source of the model and dataset, but it's important to be able to understand ways in which the model can be improved as its output directly correlates to the quality of the user experience.
In the context of this project, we can augment the model using an existing pre-trained image classifier as the encoder, as mentioned earlier. This not only fast-tracks training, providing more opportunities to iterate, but also is likely to improve performance...