Machine learning (ML) models often face challenges when deployed into production due to real-world factors such as changing customer behavior, market conditions, and data discrepancies that can lead to decreased accuracy and reliability. Model monitoring is an essential phase in the ML lifecycle, designed to detect data drift, concept drift, and model degradation, ensuring models remain effective in addressing initial problems. Challenges like data skew, model staleness, and feedback loops can degrade a model's performance over time, necessitating continuous monitoring and retraining with updated datasets. Effective model monitoring requires collaboration between data science and operations teams, comprehensive logging of pipeline activities, and tracking of both data/model quality and system health metrics. Setting alerts for critical issues is crucial, and a robust model production monitoring platform can efficiently capture errors and anomalies, providing insights into model performance and system status. Despite the nascent stage of machine learning, tools like Comet's Model Production Monitoring offer real-time performance tracking, aiding organizations in managing the complexities of maintaining ML models in production environments.