Alerts and automated retraining are essential components in modern machine learning and data management systems. Alerts serve as notifications that inform users about critical events or thresholds, such as when inventory levels drop below a certain point or when model performance metrics indicate a need for action. Automated retraining, on the other hand, refers to the process of updating machine learning models dynamically based on performance metrics or data changes, rather than relying on fixed schedules. Together, these mechanisms enhance the efficiency and effectiveness of systems, ensuring that models remain accurate and responsive to evolving data landscapes.