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

H2O MOJO deep dive

All MOJOs are fundamentally similar from a deployment and scoring standpoint. This is true regardless of the MOJO's origin from an upstream model-building standpoint, that is, regardless of which of H2O's wide diversity of model-building algorithms (for example, Generalized Linear Model, and XGBoost) and techniques (for example, Stacked Ensembles and AutoML) and training dataset sizes (from GBs to TBs) were used to build the final model.

Let's get to know the MOJO in greater detail.

What is a MOJO?

A MOJO stands for Model Object, Optimized. It is exported from your model-building IDE by running the following line of code:

model.download_mojo(path="path/for/my/mojo")

This downloads a uniquely-named .zip file onto the filesystem of your IDE, to the path you specified. This .zip file is the MOJO and this is what is deployed. You do not unzip it, but if you are curious, it contains a model.ini file that describes the MOJO as well...

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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

H2O MOJO deep dive

What is a MOJO?

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Authors (2)

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