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

Product type Paperback

Published in Jul 2022

Publisher Packt

ISBN-13 9781800566019

Length 396 pages

Edition 1st Edition

Tools

H2O

Concepts

Machine Learning

Authors (2):

Gregory Keys

David Whiting

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Table of Contents (22) Chapters

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

A model building and deployment view – the personas on the ground

Many personas or stakeholders are involved in the machine learning life cycle. The key personas involved in H2O at scale model building and deployment are the data scientist, the Enterprise Steam administrator, and the operations team. The focus of this chapter is on these personas. Let's get a high-level view of their activities as shown in the following diagram:

Figure 11.1 – Key personas involved in building and deploying H2O models at scale

A summary of these stakeholders and their high-level concerns are as follows:

Enterprise Steam Administrator: Governs who launches H2O clusters on the multitenant enterprise server cluster and how many resources they are allowed to consume, and centralizes and manages H2O integration on the server cluster
H2O Cluster Operations: Troubleshoots H2O jobs on the enterprise server cluster (either a Kubernetes or Hadoop cluster...

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

Table of Contents (22) Chapters

A model building and deployment view – the personas on the ground

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Authors (2)

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