Monitoring Machine Learning Concepts

Overview

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

Machine Learning Monitoring Concepts

Machine learning models influence more and more decisions in the real world. These models need monitoring to prevent failure and ensure that they provide business value to your company. This course will introduce you to the fundamental concepts of creating a robust monitoring system for your models in production.

Discover the Ideal Monitoring Workflow

The course starts with the blueprint of where to begin monitoring in production and how to structure the processes around it. We will cover basic workflow by showing you how to detect the issues, identify root causes, and resolve them with real-world examples.

Explore the Challenges of Monitoring Models in Production

Deploying a model in production is just the beginning of the model lifecycle. Even if it performs well during development, it can fail due to continuously changing production data. In this course, you will explore the difficulties of monitoring a model’s performance, especially when there’s no ground truth.

Understand in Detail Covariate Shift and Concept Drift

The last part of this course will focus on two types of silent model failure. You will understand in detail the different kinds of covariate shifts and concept drift, their influence on the model performance, and how to detect and prevent them.

Syllabus

What is ML Monitoring

The first chapter will explain why businesses need to monitor your machine learning models in production. You will learn about the ideal monitoring workflow and the steps involved, as well as some of the challenges that monitoring systems can face in production.

Theoretical Concepts of monitoring

In Chapter 2, you'll discover the fundamental importance of performance monitoring in a reliable monitoring system. We'll explore the common challenges faced in real-world production environments, such as the availability of ground truth. By the end of the chapter, you'll know how to handle situations when ground truth data is delayed or absent , using performance estimation algorithms.

Covariate Shift and Concept Drift Detection

Now that you know the basics of covariate shift and concept drift in production, let''s dive a little bit deeper. At the end of this chapter, you will know the different ways to detect and handle them in real-world scenarios.