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

Surveying a sample of MOJO deployment patterns

The purpose of this chapter is to overview the diverse ways in which MOJOs can be deployed for making predictions. Enough detail is given to provide an understanding of the context of MOJO deployment and scoring. Links are provided to find low-level details.

First, let's summarize our sample of MOJO scoring patterns in table form to get a sense of the many different ways you can deploy MOJOs. After this sample overview, we will elaborate on each table entry more fully.

Note that the table columns for our deployment-pattern summaries are represented as follows:

Data Velocity: This refers to the size and speed of data that is scored and is categorized as either real-time (single record scored, typically in less than 100 milliseconds), batch (large numbers of records scored at one time), and streaming (a continuous flow of records that are scored).
Scoring Communication: This refers to how the scoring is triggered...

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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

Surveying a sample of MOJO deployment patterns

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Authors (2)

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