In this chapter, we learned about the important topic of how to build up our solutions for training and staging 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. I termed this the “Model Factory.”
We got into the more technical details of some important concepts with a deep dive into what training an ML model really means, which we framed as learning about how ML models learn. Some time was then spent on the key concepts of feature engineering, or how you transform your data into something that a ML model can understand during this process. This was followed by sections on how to think about the different modes your training system can run in, which I termed “train-persist” and “train-run.”
We then discussed how you can perform drift detection on...