Example Concept: Feature drift occurs when the statistical properties of input features change over time, potentially leading to model performance degradation. Detecting feature drift involves monitoring metrics such as mean, variance, or distribution shifts of features using statistical tests or machine learning approaches. By identifying drift early, data scientists can retrain models, update features, or adjust preprocessing steps to maintain model accuracy and relevance.