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

Chapter 9: Production Scoring and the H2O MOJO

We spent the entire previous section learning how to build world-class models against data at scale with H2O. In this chapter, we will learn how to deploy these models and make predictions from them. First, we will cover the background on putting models into production scoring systems. We will then learn how H2O makes this easy and flexible. At the center of this story is the H2O MOJO (short for Model Object, Optimized), a ready-to-deploy scoring artifact that you export from your model building environment. We will learn technically what a MOJO is and how to deploy it. We will then code a simple batch file scoring program and embed a MOJO in it. We will finish with some final notes on the MOJO. Altogether, in this chapter, you will develop the knowledge to deploy H2O models in diverse ways and so begin achieving value from live predictions.

These are the main topics we will cover in this chapter:

Relating the model building...

The rest of the chapter is locked

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 9: Production Scoring and the H2O MOJO

Unlock this book and the full library FREE for 7 days

Authors (2)

Personalised recommendations for you