Importance of features in production
Before discussing how to bring features to production, let's understand why features are needed in production. Let's go through an example.
We often use taxi and food delivery services. One of the good things about these services is that they tell us how long it will take for our taxi or food to arrive. Also, most of the time, it is approximately correct. How does it predict this accurately? It uses ML, of course. The ML model predicts how long it will take for the taxi or food to arrive. For a model like that to be successful, not only does it need a good feature engineering and ML algorithm, but also the most recent features. Though we don't know the exact feature set that the model uses, let's look at a couple of features that change dynamically and are very important.
With food delivery services, the major components that affect the delivery time are restaurants, drivers, traffic, and customers. The model probably...