Answer
Teams compare the statistical patterns of new incoming data against the original training data, watching for meaningful differences that suggest the model may no longer fit current conditions.
Real-world example A retailer detects model drift when customer buying patterns shift after a major holiday season, causing the old recommendation model to become less accurate.