so to be more clear lets consider the problem of loan default prediction. Let's say I have trained and tested off-line multiple classifiers and ensembled them. Then I gave this model to production.
But because people change, data and many other factors change as well. And performance of our model eventually will decrease. So then it needs to be replaced with the new, better model.
What are the common techniques, model stability tests, model performance tests, metrics after deployment? How to decide when to replace current model with newer one?