Concept drift in machine learning 101

Demand Forecasting using Machine Learning (ML) models presents significant potential for retailers to increase revenue and streamline business operations. However, as these ML models become more popular for automation and prediction tasks, concept drift becomes a critical issue that can degrade model performance over time. Concept drift refers to the change in statistical properties of a target variable, which may result from sudden, gradual, or incremental changes in the input data or true labels. Retailers must monitor their models' performance and detect concept drift by examining evaluation metrics and identifying virtual or real drifts. Regularly retraining models can help alleviate some issues caused by concept drift, but staying on top of it with monitoring tools is crucial to avoid significant degradation in model performance.