We will discuss why and how you need to monitor the performance of ML in production. Using real-life use cases, we'll explain concept and data drift and describe a range of tools used to detect it. You'll learn how the concept and the data drift can adversely influence model performance and how to estimate and predict performance of ML without full access to ground truth data.