Deploying machine learning models from proof of concept to production involves significant complexity beyond initial deployment, requiring continuous monitoring and improvement to ensure the models deliver business value. Machine learning model monitoring tools, such as Arize AI, JFrog ML, WhyLabs, Evidently AI, Neptune AI, Qualdo, and Fiddler AI, play a crucial role in tracking model performance, identifying issues like data drift, input data problems, and model degradation, and providing insights through intuitive dashboards and alerts. These tools offer features like A/B test management, visualization, real-time monitoring, and explainability, enabling organizations to manage model availability, capture inference performance, and facilitate continuous training and evaluation. While some tools are comprehensive MLOps solutions with integrated monitoring, others focus specifically on observability and debugging, helping teams maintain and optimize machine learning models to sustain high performance and reliability in production environments. JFrog ML, for instance, offers a unified platform for managing the entire AI/ML lifecycle, embedding DevSecOps practices to ensure model traceability and compliance, thereby enhancing collaboration and safeguarding AI/ML assets in production.