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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

Summary

In this short chapter, we wanted to step back and show you the big picture of the platform and the model life cycle. We encourage you to refer to Chapter 2, Understanding MLOps, where we presented a typical ML life cycle, for a more detailed discussion. Recall the importance of collaborations across multiple teams and how investing more time in understanding the available data will result in a model that delivers the expected business value.

Now you know what the various stages of your project will look like. In the next two chapters, you will implement the flight delay prediction service using the ML platform that we have presented in this book and you will perform each of the stages we have described in this chapter. The idea is to show you how the platform caters to every stage of your project and how you can implement this platform in your organization.

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