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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

Defining a self-service platform

Self-service is defined as the capability of a platform that allows platform end users to provision resources on-demand without other human intervention. Take, for example, a data scientist user who needs an instance of a Jupyter notebook server, running on a host container with eight CPUs, to perform his/her work. A self-service ML platform should allow the data scientist to provision, through an end user friendly interface, the container that will run an instance of the Jupyter notebook server on it. Another example of self-service provisioning would be a data engineer requesting a new instance of an Apache Spark cluster to be provisioned to run his/her data pipelines. The last example is a data scientist who wants to package and deploy their ML model as a REST service so that the application can use the model.

One benefit of a self-service platform is that it allows cross-functional teams to work together with minimal dependencies on other teams...

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