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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

Reproducibility

Now, you know what an ML life cycle would look like and how the platform assists you in every step of your journey. As an individual, you may be able to write every step of the data pipelines and model training and tuning in a single notebook. However, this may cause a problem in teams where different people are working on different parts of the life cycle. Let's say someone wants to run the model training part but the entire process is tied up with one another. Your team may not be able to scale with this approach.

A better and more scalable approach is to write different notebooks for various stages (such as data processing and model training) in your project life cycle and use a workflow engine to tie them up. Using the Kubernetes platform, all the stages will be executed using containers and provide a consistent environment for your project between different runs. The platform provides Airflow, an engine that could be used for creating and executing workflows...

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