Detecting model drift requires a point of comparison and real-time visibility. The AAIATM manual identifies the best practice as "Establishing a performance baseline" (using metrics like accuracy or F1-score from the initial validation phase) and then "Implementing continuous monitoring" to track those metrics as the model processes live production data. Any significant deviation from the baseline serves as an early warning that the model's environment has shifted and retraining is necessary.