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Machine Learning at Scale with H2O

You're reading from   Machine Learning at Scale with H2O A practical guide to building and deploying machine learning models on enterprise systems

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781800566019
Length 396 pages
Edition 1st Edition
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Authors (2):
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Gregory Keys Gregory Keys
Author Profile Icon Gregory Keys
Gregory Keys
David Whiting David Whiting
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David Whiting
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Table of Contents (22) Chapters Close

Preface 1. Section 1 – Introduction to the H2O Machine Learning Platform for Data at Scale
2. Chapter 1: Opportunities and Challenges FREE CHAPTER 3. Chapter 2: Platform Components and Key Concepts 4. Chapter 3: Fundamental Workflow – Data to Deployable Model 5. Section 2 – Building State-of-the-Art Models on Large Data Volumes Using H2O
6. Chapter 4: H2O Model Building at Scale – Capability Articulation 7. Chapter 5: Advanced Model Building – Part I 8. Chapter 6: Advanced Model Building – Part II 9. Chapter 7: Understanding ML Models 10. Chapter 8: Putting It All Together 11. Section 3 – Deploying Your Models to Production Environments
12. Chapter 9: Production Scoring and the H2O MOJO 13. Chapter 10: H2O Model Deployment Patterns 14. Section 4 – Enterprise Stakeholder Perspectives
15. Chapter 11: The Administrator and Operations Views 16. Chapter 12: The Enterprise Architect and Security Views 17. Section 5 – Broadening the View – Data to AI Applications with the H2O AI Cloud Platform
18. Chapter 13: Introducing H2O AI Cloud 19. Chapter 14: H2O at Scale in a Larger Platform Context 20. Other Books You May Enjoy Appendix : Alternative Methods to Launch H2O Clusters

View 3 – The data scientist

The primary ways that data scientists using H2O at scale interact with the Enterprise Steam administrator and the operations teams are shown in the following diagram:

Figure 11.6 – Data scientist interactions with administrator and operations stakeholders when using H2O at scale

Let's drill down into the key data scientist interactions with the Enterprise Steam administrator and with operations teams.

Interactions with Enterprise Steam administrators

Recall that data scientists authenticate through Enterprise Steam and launch H2O cluster sizes as defined by Enterprise Steam profiles. Data scientists may at times interact with Enterprise Steam administrators to solve authentication issues or to ask for profiles that size H2O clusters more appropriately to their needs. They may also request access to a different YARN queue than what is allowed in their profile, request a longer configured idle time for their...

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