Drift Detection

Drift detection is the process of monitoring deployed AI models and their input data to identify when statistical properties have changed significantly from the distributions present during training—a phenomenon called drift. Data drift occurs when the distribution of input features changes over time (for example, a shift in the types of products being searched). Concept drift occurs when the relationship between inputs and the target variable changes (for example, patterns that previously indicated fraud no longer do so, or vice versa). Model performance drift is the downstream manifestation: declining accuracy, precision, recall, or business metric performance.

In commerce AI, drift detection is an operational necessity rather than a nice-to-have. Models trained on pre-pandemic shopping patterns drift significantly as consumer behavior changes. Seasonal fluctuations, competitor promotions, and macroeconomic shifts all alter the data distributions that models were trained on. A recommendation model exhibiting concept drift might continue returning results confidently while systematically suggesting items that are no longer relevant, relevant inventory that's out of stock, or missing emerging demand signals. Drift detection systems trigger alerts and retraining pipelines when distributional shifts exceed defined thresholds, ensuring that models operating in production remain accurate and that degradation is caught proactively—before it manifests as measurable revenue or customer satisfaction impact.