Alerts and automated retraining are essential components in modern machine learning and data management systems. Alerts serve as notifications that inform data scientists and engineers about significant changes or anomalies in model performance, data quality, or operational metrics. These alerts enable timely interventions to maintain model accuracy and reliability. Automated retraining, on the other hand, refers to the process of automatically updating machine learning models based on new data or performance metrics. This dynamic approach ensures that models remain relevant and effective over time, adapting to changes in data patterns without requiring manual intervention. Together, these practices enhance the robustness and efficiency of AI systems.