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

Summary

We began this chapter by taking a high-level view of the transition from model building to model deployment. We saw that this transition is bridged for H2O by the MOJO, a deployable representation of the trained model that is easy to generate from model building and easy to deploy for fast model scoring.

We then took a closer look at the range of target systems MOJOs can be deployed on, and saw that these must run in a Java runtime but, otherwise, are quite diverse. MOJOs can be scored on real-time, batch, and streaming systems, usefully categorized as H2O Scorers (scoring software provided and supported by H2O), third-party integrations (software provided and supported by companies other than H2O), and your software integrations (software that you build and maintain).

This categorization of target systems helps us determine whether you can deploy the exported MOJO directly, or whether you need to wrap it in a Java class using the h2o-genmodel API to embed it into the...

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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

Summary

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