Model drift refers to the phenomenon where the performance of a machine learning model degrades over time due to changes in the statistical properties of the input features or the target variable. This can occur for various reasons, such as shifts in the underlying data distribution or changes in the relationship between inputs and outputs, known as concept drift.

There are different types of model drift, with concept drift being the most recognized. Concept drift occurs when the intrinsic relationship between the model’s inputs and outputs changes, leading to a decline in prediction accuracy. Monitoring and addressing model drift is crucial for maintaining the effectiveness of machine learning solutions over time, as it can significantly impact decision-making processes.

To manage model drift, practitioners often employ monitoring tools and techniques to detect changes in model performance and take corrective actions as needed ²⁴⁵.