Retraining Machine Learning Model Approaches

There are different approaches for identifying model drifts and model retraining approaches but there is no one standard method for all kinds of problems. When developing any ML model, it is important to understand how data data will change over time. Data distribution of all the features should remain constant, but this will not be the case always. Data that has been used to train model which predict the prices of house some months ago will not give great prediction today. We need up to date information to train the models. To identify model drift is to explicitly determine that predictive performance has deteriorated.