Machine Learning Models: Detecting Silent Failures

Learn about machine learning models and how to detect silent features in these models.

This session will teach you how to detect silent machine-learning model failure without accessing the target data. We will cover the most likely causes of the Machine Learning model's failure, like data and concept drift. You'll learn the tools (both statistical and algorithmic) used in detecting and dealing with these failures, their applications, and their limits.

By the end of the webinar you will be able to:

- Detect drop in performance of their ML models without using ground truth data
- Monitor their Machine Learning models
- Understand and detect data drift to resolve the issues found

Table of Contents:

00:00 Introduction
05:33 Data drift and concept drift
10:17 Performance estimation
23:29 Data and concept drift detection
31:27 Summary
39:05 Demo
53:30 QnA

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