Machine learning models must adapt to ever-changing data, necessitating regular updates to maintain their predictive capabilities. Effective model performance monitoring is crucial for this process, utilizing tools that provide insights and statistics to improve models, although not every tool suits every use case. Setting realistic goals and selecting appropriate metrics are essential, as metrics like mean squared error may be sensitive to outliers. Modularizing data preprocessing and model training can simplify updates, while using baseline models aids in identifying overfitting or data drift. Strategic retraining, leveraging ensemble models or neural networks, can enhance performance without starting from scratch. Tools like neptune.ai, Evidently, and others offer visualization and analytics features essential for monitoring, but developing a tailored strategy is necessary to fully integrate monitoring into the deployment pipeline, requiring collaboration among data scientists, business leaders, and domain experts.