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

Chapter 10: H2O Model Deployment Patterns

In the previous chapter, we learned how easy it is to generate a ready-to-deploy scoring artifact from our model-building step and how this artifact, called a MOJO, is designed to flexibly deploy to a wide diversity of production systems.

In this chapter, we explore this flexibility of MOJO deployment by surveying a wide range of MOJO deployment patterns and digging down into the details of each deployment pattern. We will see how MOJOs are implemented for scoring on either H2O software, third-party software including business intelligence (BI) tools, and your own software. These implementations will include scoring on real-time, batch, and streaming data.

Recall from Chapter 1, Opportunities and Challenges, how machine learning (ML) models achieve business value when deployed to production systems. The knowledge you gain in this chapter will allow you to find the appropriate MOJO deployment pattern for a particular business case.&...

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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

Chapter 10: H2O Model Deployment Patterns

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