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Machine Learning at Scale with H2O

You're reading from   Machine Learning at Scale with H2O A practical guide to building and deploying machine learning models on enterprise systems

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781800566019
Length 396 pages
Edition 1st Edition
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Authors (2):
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Gregory Keys Gregory Keys
Author Profile Icon Gregory Keys
Gregory Keys
David Whiting David Whiting
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David Whiting
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Table of Contents (22) Chapters Close

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 production scoring

Models achieve their business value when they are put into production to make predictions (or generate unsupervised results for an unsupervised class of problems). We discuss, in this section, a more detailed view of the H2O pipeline from model building to production scoring.

End-to-end production scoring pipeline with H2O

Take a look at the following diagram showing an end-to-end H2O pipeline from model training to model deployment and production scoring:

Figure 9.3 – High-level view of full scoring pipeline with H2O

Typically, model building is considered a development (DEV) environment, and model scoring is a PROD environment with source data from each respective environment.

For DEV, we have treated feature engineering and model training (and many associated steps such as model explainability and evaluation) extensively in Section 2, Building State-of-the-Art Models on Large Data Volumes Using H2O. We also briefly...

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