Model Drift and Retraining

They are the same thing; both mean the model needs more training data Data drift is a shift in input distributions (P(X) changes); concept drift is a shift in the target relationship (P(Y|X) changes); they require different detection and retraining strategies Data drift only affects unsupervised models; concept drift only affects supervised models Neither type of drift requires retraining if the model was trained on enough data initially