Monitoring and observability have become critical components in the successful deployment and maintenance of machine learning systems in production. This article presents a comprehensive framework for implementing robust ML observability, covering foundational principles, model performance tracking, drift detection, operational health monitoring, fairness evaluation, and platform construction. It explores both technical implementation details and strategic considerations for ML teams looking to enhance their monitoring capabilities. The proposed architecture emphasizes proactive detection of issues before they impact users, through continuous tracking of model behaviors, input data characteristics, and system health metrics. By following these guidelines, organizations can build resilient ML systems that maintain performance, fairness, and reliability throughout their lifecycle in production environments.