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

View More author details

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

Exploring examples of MOJO scoring with your target-system software

In addition to deploying MOJOs for scoring on H2O and third-party software, you can also take a Do-It-Yourself (DIY) approach and deploy scoring in your own software. Let's see how to do this.

Your software application

There are two ways to score from your own software: integrate with an external scoring system or embed scoring directly in your software system.

The following diagram shows the pattern of integrating with an external scoring system:

Figure 10.12 – MOJO application-scoring pattern for external scoring

This pattern should look familiar because it is fundamentally the same as what we saw with scoring from BI tools: your software acts as a client to consume MOJO predictions made from another system. The external prediction system can be a MOJO deployed on a REST server (for example, an H2O REST scorer) or batch database scorer (for example, a Snowflake Java UDF...

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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

Exploring examples of MOJO scoring with your target-system software

Your software application

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Authors (2)

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