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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

Chapter 11: Machine Learning on Kubernetes

Throughout the chapters, you have learned about the differences between a traditional software development process and machine learning (ML). You have learned about the ML life cycle and you understand that it is pretty different from the conventional software development life cycle. We have shown you how open source software can be used to build a complete ML platform on Kubernetes. We presented to you the life cycle of ML projects, and by doing the activities, you have experienced how each phase of the project life cycle is executed.

In this chapter, we will show you some of the key ideas that we wanted to bring forth to further your knowledge on the subject. The following topics will be covered in this chapter:

  • Identifying ML platform use cases
  • Operationalizing ML
  • Running on Kubernetes

These topics will help you decide when and where to use the ML platform that we presented in this book and help you set up the...

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