When should you retrain a production machine learning model?

Example Concept: Retraining a model should be considered when there is a noticeable degradation in performance metrics such as accuracy, precision, recall, or F1 score, indicating that the model no longer generalizes well to new data. Additionally, if data drift is detected — where the statistical properties of the input data change over time — or if there are significant updates in business objectives or data sources, retraining becomes necessary to align the model with current conditions.