The text discusses the challenges of model and data drift in machine learning systems, highlighting how these issues can silently degrade performance and impact business outcomes. It differentiates between data drift, which involves changes in input data distributions without altering the model's logic, and model drift, where the fundamental relationships the model learned no longer hold true. The article emphasizes the importance of correctly diagnosing the type of drift to avoid costly and time-consuming troubleshooting. It outlines various detection and mitigation strategies, such as using statistical tests for data drift and employing shadow models and proxy metrics for model drift. The text also advises on the organizational responsibilities for managing these drifts and suggests allocating a significant portion of ML capacity to drift management to prevent silent failures. Best practices for drift detection include automated class boundary detection, advanced alert systems, visualization of drift analytics, data error potential scoring, and leveraging comprehensive observability platforms. The article concludes by promoting Galileo's Agent Observability Platform as a solution for effective drift detection and monitoring, offering features like automated distribution monitoring and customizable recovery pipelines to enhance ML system reliability and performance.