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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

Using a custom notebook image

As you have seen in Chapter 5, Data Engineering, JupyterHub allows you to spin up Jupyter Notebook-based development environments in a self-service manner. You have launched the Base Elyra Notebook Image container image and used it to write the data processing code using Apache Spark. This approach enables your team to use a consistent or standardized development environment (for example, same Python versions and same libraries for building code) and apply security policies to the known set of software being used by your team. However, you may also want to create your own custom images with a different set of libraries or a different ML framework. The platform allows you to do that.

In the following subsection, you will build and deploy a custom container image to be used within your team.

Building a custom notebook container image

Let's assume that your team wants to use a specific version of the Scikit library along with some other supporting...

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