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Machine Learning on Kubernetes

You're reading from   Machine Learning on Kubernetes A practical handbook for building and using a complete open source machine learning platform on Kubernetes

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781803241807
Length 384 pages
Edition 1st Edition
Languages
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Authors (2):
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Ross Brigoli Ross Brigoli
Author Profile Icon Ross Brigoli
Ross Brigoli
Faisal Masood Faisal Masood
Author Profile Icon Faisal Masood
Faisal Masood
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Toc

Table of Contents (16) Chapters Close

Preface 1. Part 1: The Challenges of Adopting ML and Understanding MLOps (What and Why)
2. Chapter 1: Challenges in Machine Learning FREE CHAPTER 3. Chapter 2: Understanding MLOps 4. Chapter 3: Exploring Kubernetes 5. Part 2: The Building Blocks of an MLOps Platform and How to Build One on Kubernetes
6. Chapter 4: The Anatomy of a Machine Learning Platform 7. Chapter 5: Data Engineering 8. Chapter 6: Machine Learning Engineering 9. Chapter 7: Model Deployment and Automation 10. Part 3: How to Use the MLOps Platform and Build a Full End-to-End Project Using the New Platform
11. Chapter 8: Building a Complete ML Project Using the Platform 12. Chapter 9: Building Your Data Pipeline 13. Chapter 10: Building, Deploying, and Monitoring Your Model 14. Chapter 11: Machine Learning on Kubernetes 15. Other Books You May Enjoy

Monitoring your model

In this last section, you will see how the platform automatically starts capturing the typical performance metrics of your model. The platform also helps you visualize the performance of the inference. The platform uses Seldon to package the model, and Seldon exposes default metrics to be captured. Seldon also allows you to write custom metrics for specific models; however, it is out of the scope of this book.

Let's start by understanding how the metrics capture and visualization work.

Understanding monitoring components

The way metrics capture works is that your model is wrapped by Seldon. Seldon then exposes the metrics to a well-defined URL endpoint, which was detailed in Chapter 7, Model Deployment and Automation. Prometheus harvests this information and stores it in its database. The platform's Grafana connects to Prometheus and helps you visualize the recorded metrics.

Figure 10.47 summarizes the relationship between the model and monitoring...

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