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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

Roadmap

ODH is an active open source project primarily maintained by Red Hat, the largest open source company in the world. ODH will keep getting updated to bring more and more features to the product. However, because the ML and MLOps space is also relatively new and still evolving, it is not unnatural to see significant changes and pivots in the project over time.

As of writing this book, the next version of ODH includes the following changes (as shown in Figure 11.2):

Figure 11.2 – ODH's next release

There are other features of ODH that you have not yet explored because they are more geared toward data engineering and the data analytics space. One example is data virtualization and visualization using Trino and Superset. If you want to learn more about these features, you can explore them in the same ML platform you built by simply updating the kfdef file to include Trino and Superset as components of your ML platform. You will find some examples...

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