Persisting your models
In the previous chapter, we introduced some of the basics of model version control using MLflow. In particular, we discussed how to log metrics for your ML experiments using the MLflow Tracking API. We are now going to build on this knowledge and consider the touchpoints our training systems should have with model control systems in general.
First, let's recap what we're trying to do with the training system. We want to automate (as far as possible) a lot of the work that was done by the data scientists in finding the first working model, so that we can continually update and create new model versions that still solve the problem in the future. We would also like to have a simple mechanism that allows the results of the training process to be shared with the part of the solution that will carry out the prediction when in production. We can think of our model version control system as a bridge between the different stages of the ML development process...