How do data scientists detect concept drift in production models?

Example Concept: Concept drift detection involves continuously monitoring model predictions and actual outcomes to identify discrepancies. Techniques such as statistical hypothesis testing (e.g., Kolmogorov-Smirnov test), monitoring prediction error rates, or using drift detection algorithms like the Page-Hinkley test can be employed. These methods help in identifying shifts in data distribution or changes in the relationship between input features and the target variable, indicating potential drift.