Wrap-up

1. Wrap-up

Congratulations on reaching the end of the course! Now, let's discuss what you have learned.

2. Chapter 1 - What Is ML Monitoring?

In Chapter 1, you learned about the ideal monitoring workflow, with the technical performance evaluation at its core. You also discovered the importance of monitoring machine learning models in production and the potential challenges that may arise.

3. Chapter 2 - Theoretical Concepts of Monitoring

In Chapter 2, we delved deeper into why covariate shift detection is not an effective approximation of performance. Instead, you learned how to estimate performance when ground truth is not available using Confidence Based Performance Estimation and Direct Loss Estimation algorithms.

4. Chapter 3 - Covariate Shift and Concept Drift

Chapter 3 provided a detailed understanding of covariate shift, including two detection methods: multivariate and univariate. You also gained insights into the concept of drift and how to address it in a production environment.

5. Congratulations!

Thank you for investing your time in this course. You are now equipped with the knowledge to build a robust monitoring system in production. It has been a great journey working with you, and I wish you all the best in your future endeavors. Bye!

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This exercise is part of the course

Monitoring Machine Learning Concepts

IntermediateSkill Level

4.8+

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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.

Exercise 1: Why you need to monitor your model Exercise 2: Why models fail?Exercise 3: The benefits of monitoring systems Exercise 4: The ideal monitoring workflow Exercise 5: The importance of monitoring KPIs Exercise 6: Ideal monitoring workflow Exercise 7: Monitoring workflow in real-life scenario Exercise 8: Challenges of monitoring ML models Exercise 9: Delayed ground truth Exercise 10: Covariate shift vs concept drift

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.

Exercise 1: Monitoring technical performance directly Exercise 2: Why performance first?Exercise 3: Different covariate shifts Exercise 4: Availability of ground truth Exercise 5: Is performance estimation required?Exercise 6: Performance monitoring in production Exercise 7: Performance estimation Exercise 8: CBPE considerations Exercise 9: Performance estimation in production Exercise 10: Algorithms for performance estimation

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.

Exercise 1: What is covariate shift?Exercise 2: Swapped features Exercise 3: Drift detection method Exercise 4: How to detect covariate shift Exercise 5: Subtle changes Exercise 6: Different methods Exercise 7: What is concept drift?Exercise 8: Type of concept drift Exercise 9: Concept drift and covariate shift interaction Exercise 10: How to handle concept drift?Exercise 11: Real-time adaptation Exercise 12: Concept drift detection and resolution Exercise 13: Wrap-up

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