Data Drift

Data drift occurs when the statistical properties of the input data change, leading to a decline in the performance of a machine learning model. This phenomenon can result from various factors, including shifts in user behavior, market conditions, or external events. Monitoring for data drift is crucial for maintaining model accuracy, and strategies such as retraining or updating the model may be necessary to address these changes.