Example Concept: In production, model performance is typically evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC for classification tasks, or RMSE and MAE for regression tasks. Monitoring involves setting up dashboards to track these metrics over time, implementing alert systems for significant deviations, and using A/B testing to compare model versions. Additionally, feedback loops from user interactions can help refine the model continuously.