Machine Learning Monitoring Concepts As machine learning models become increasingly influential in real-world decision-making, it is crucial to implement monitoring systems to prevent failures and ensure their ongoing business value. This course aims to provide a comprehensive understanding of the fundamental concepts involved in creating a robust monitoring system for models in production. The course begins by outlining the ideal workflow for monitoring in production and establishing effective processes. Through real-world examples, participants will learn how to detect issues, identify their root causes, and implement appropriate resolutions. Deploying a model in production is only the initial step in its lifecycle. Even if a model performs well during development, it can encounter failures due to the ever-changing nature of production data. This course delves into the challenges of monitoring a model's performance, particularly when there is no ground truth available. The final part of the course focuses on two types of silent model failure: covariate shift and concept drift. Participants will gain a detailed understanding of these phenomena, their impact on model performance, and techniques for detecting and preventing them. By the end of this course, participants will possess the knowledge and skills necessary to establish a robust monitoring system for machine learning models in production, ensuring their continued success and value to the organization.