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Model drift refers to the model’s performance changing over time as the data it sees shifts and the relationship between inputs and outputs evolves. In production, user behavior, market conditions, seasonality, or other underlying processes can alter data patterns, causing predictions to become less accurate or biased even if the model was well-trained initially.

Because these shifts can occur gradually and unpredictably, continuous monitoring is essential to catch them early. Ongoing checks track performance metrics and detect changes in data distributions or in how features relate to targets. When drift is detected, you can retrain, update, or replace components to restore or maintain accuracy, reliability, and safety. Techniques include tracking performance over time, using drift detectors, and setting retraining triggers based on predefined thresholds.

Other interpretations, like changes in model architecture, or unrelated operational factors such as data center temperature or budget drift, don’t capture the tendency of a model’s behavior to change due to shifting data patterns.