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 or data quality, enabling timely interventions. Automated retraining, on the other hand, refers to the process of updating machine learning models dynamically based on real-time data and performance metrics. This approach helps maintain model accuracy and relevance, reducing the risks associated with stale models. Together, alerts and automated retraining enhance the efficiency and effectiveness of machine learning applications, ensuring they adapt to evolving data landscapes.