Data Drift

Drift can concern features, missingness, categories, text characteristics or predictions, depending on what is compared. Statistical tests, distances or discriminators can detect different forms of change. The reference and current windows define the comparison, while sample size and threshold affect sensitivity. Data drift differs from concept drift, where the relationship between inputs and the target changes. An input distribution can shift without harming a model, and target relationships can change even when simple feature summaries look stable. The distinction prevents treating every distribution alert as direct evidence of accuracy loss.

The practitioner selects meaningful reference and current windows, validates data types and monitors missing or newly introduced values separately where needed. It chooses tests suitable for the variable and checks false alarms under expected seasonal variation. Useful artifacts include drift reports, thresholds and an investigation procedure linked to quality or outcome measurements. Alerts should identify affected features and populations. Before retraining, the team checks for pipeline defects, changes in users or genuine behavior shifts, then assesses whether these changes affect the task the model performs.

Large samples can flag small harmless differences, while small samples may miss important shifts. Marginal feature tests can also miss changed joint relationships. Alert thresholds require context and cannot be copied as universal standards. Drift should be investigated with data quality and task performance, avoiding automatic retraining based on one statistic. Its value lies in identifying a change worth examining, with explicit uncertainty about the effect on model behavior.