Monitoring Machine Learning in Python

Overview

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This course covers everything you need to know to build a basic machine learning monitoring system in Python

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.

Syllabus

Data Preparation and Performance Estimation

In this chapter, you will be introduced to the NannyML library and its fundamental functions. Initially, you will learn the process of preparing raw data to create reference and analysis sets ready for production monitoring. As a practical example, you will investigate predicting the tip amount for taxi rides in New York. Toward the end of the chapter, you will also discover how to estimate the performance of the tip prediction model using NannyML.

Monitoring Performance and Business Value

In this chapter, you will be introduced to realized performance calculators used when ground truth becomes available. You will learn about the more advanced methods for handling results, including filtering, plotting, converting them to data frames, chunking, and establishing custom thresholds. Lastly, you'll apply this knowledge to calculate the business value of a model trained on the hotel booking dataset.

Root Cause Analysis and Issue Resolution

Having detected the performance degradation in the hotel booking model, you will now learn how to identify the underlying issue causing it. In this chapter, you will be introduced to multivariate and univariate drift detection methods. You will also learn how to identify data quality issues and how to address the underlying problems you detect.