Continuous monitoring of machine learning (ML) models is a critical practice that ensures these models remain effective and relevant after deployment. As ML models operate in dynamic environments, they can experience data drift, where the statistical properties of input data change over time, potentially degrading performance. Continuous monitoring involves tracking various metrics, such as model accuracy, response time, and resource utilization, to detect anomalies and performance issues proactively. By implementing a robust monitoring framework, organizations can maintain model integrity, facilitate timely retraining, and ultimately enhance decision-making processes driven by ML systems. This practice is essential for sustaining user trust and achieving business objectives.