Monitoring and Performance Debugging for ML

ML models can degrade in production without proper monitoring. This course will teach you how to detect data drift, track key performance metrics, integrate monitoring into pipelines, and debug issues using visual and scheduled analysis tools.

What you'll learn

Machine learning models can degrade silently after deployment due to data drift, changing user behavior, or infrastructure failures—leading to poor decisions and loss of trust. In this course, Monitoring and Performance Debugging for ML, you’ll learn to ensure the reliability and effectiveness of ML systems in production through robust monitoring and debugging techniques.

First, you’ll explore the importance of model monitoring and what can go wrong when it's neglected, including issues like prediction skew and silent model failure. Next, you’ll discover how to detect and address data drift and concept drift, as well as integrate monitoring seamlessly into existing ML pipelines and infrastructure. Finally, you’ll learn how to configure performance tracking systems, use visual debugging tools like Manifold to analyze model behavior across data slices, and implement scheduled reporting for manual performance reviews.

When you’re finished with this course, you’ll have the skills and knowledge of production-grade ML monitoring and debugging needed to maintain trustworthy, high-performing machine learning systems in real-world environments.