Learn how to monitor your ML Models in Python

Monitoring machine learning models ensures the long-term success of your machine learning projects. Monitoring can be very complex, however, there are Python packages to help us understand how our models are performing, what data has changed that might have led to a drop in performance, and give us clues on what we need to do to get our models back on track. This course covers everything you need to know to build a basic monitoring system in Python, using the popular monitor package, nannyml.

Understand the optimal monitoring workflow

Model monitoring is not only about simply calculating model performance in production. Unfortunately, it is not that easy. Especially when labels are hard to come by. This course will teach you about the optimal monitoring workflow. It will ensure that you always catch model failures, avoid alert fatigue, and quickly get to the root of the issue.

Learn how to find the root cause of model performance issues

Another important component to model monitoring is root cause analysis. This course will dive into how to use data drift detection techniques to get to the root cause of model performance issues. You will learn how to use both univariate and multivariate data drift detection techniques to uncover potential root causes of model issues.