AI Model Drift occurs when an artificial intelligence model’s performance changes because the real-world data or conditions it encounters no longer match the patterns present during training. New customer behavior, market conditions, sensor patterns, or language usage can gradually make previously reliable predictions less accurate.
Drift can involve changes in the input data, the relationship between inputs and outcomes, or the overall environment in which the model operates. Monitoring systems can detect these shifts through performance metrics and statistical signals, allowing teams to investigate, retrain, recalibrate, or replace a model before degraded performance becomes a significant operational problem.
