Summary

In this chapter, we learned about the important topic of how to build up our solutions for training and surfacing the ML models that we want to run in production. We split the components of such a solution into pieces that tackled training the models, the persistence of the models, serving the models, and triggering retraining for the models.

We conducted a detailed investigation into the reasons why you may want to separate your training and running components for performance reasons. We then discussed how you can perform drift detection on your model performance and data statistics to understand whether retraining should be triggered. This included some examples of performing drift detection using the alibi-detect and evidently.ai packages. We then summarized some of the key concepts of feature engineering, or how you transform your data into something that a ML model can understand. We then went into a deep dive on how ML models learn and what you can control about that process...