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Monitoring Machine Learning in Python
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Hands-On Model Monitoring with Python and NannyML
Maintaining machine learning reliability requires practical tooling to track performance degradation and data shifts. This advanced course demonstrates how to build a functional monitoring pipeline in Python using the NannyML library to detect data drift and diagnose root causes.
Who Is This Course For?
Designed for experienced Python developers and ML engineers seeking hands-on techniques to monitor production models when labels are scarce.
Key Takeaways
- Practical Python Workflows: Implement end-to-end monitoring routines using NannyML to maintain model quality in production.
- Performance Estimation: Track estimated model performance even when true outcome labels are difficult or delayed to acquire.
- Drift & Root Cause Analysis: Apply univariate and multivariate data drift detection to isolate performance bottlenecks and avoid alert fatigue.
About this course
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.Advertisement