Machine Learning at Scale with H2O
Language: English
Published by Packt Publishing, 2022
- Softcover
- New
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- Title
- Machine Learning at Scale with H2O
- Author
- Keys, Gregory|Whiting, David
- Publisher
- Packt Publishing
- Publication year
- 2022
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1800566018
- ISBN 13
- 9781800566019
Build predictive models using large data volumes and deploy them to production using cutting-edge techniques
Key Features
- Build highly accurate state-of-the-art machine learning models against large-scale data
- Deploy models for batch, real-time, and streaming data in a wide variety of target production systems
- Explore all the new features of the H2O AI Cloud end-to-end machine learning platform
Book Description
H2O is an open source, fast, and scalable machine learning framework that allows you to build models using big data and then easily productionalize them in diverse enterprise environments.
Machine Learning at Scale with H2O begins with an overview of the challenges faced in building machine learning models on large enterprise systems, and then addresses how H2O helps you to overcome them. You'll start by exploring H2O's in-memory distributed architecture and find out how it enables you to build highly accurate and explainable models on massive datasets using your favorite ML algorithms, language, and IDE. You'll also get to grips with the seamless integration of H2O model building and deployment with Spark using H2O Sparkling Water. You'll then learn how to easily deploy models with H2O MOJO. Next, the book shows you how H2O Enterprise Steam handles admin configurations and user management, and then helps you to identify different stakeholder perspectives that a data scientist must understand in order to succeed in an enterprise setting. Finally, you'll be introduced to the H2O AI Cloud platform and explore the entire machine learning life cycle using multiple advanced AI capabilities.
By the end of this book, you'll be able to build and deploy advanced, state-of-the-art machine learning models for your business needs.
What you will learn
- Build and deploy machine learning models using H2O
- Explore advanced model-building techniques
- Integrate Spark and H2O code using H2O Sparkling Water
- Launch self-service model building environments
- Deploy H2O models in a variety of target systems and scoring contexts
- Expand your machine learning capabilities on the H2O AI Cloud
Who this book is for
This book is for data scientists and machine learning engineers who want to gain hands-on machine learning experience by building and deploying state-of-the-art models with advanced techniques using H2O technology. An understanding of the data science process and experience in Python programming is recommended. This book will also benefit students by helping them understand how machine learning works in real-world enterprise scenarios.
Table of Contents
- Opportunities and Challenges
- Platform Components and Key Concepts
- Fundamental Workflow - Data to Deployable Model
- H2O Model Building at Scale – Capability Articulation
- Advanced Model Building – Part I
- Advanced Model Building – Part II
- Understanding ML Models
- Putting It All Together
- Production Scoring and the H2O MOJO
- H2O Model Deployment Patterns
- The Administrator and Operations Views
- The Enterprise Architect and Security Views
- Introducing the H2O AI Cloud
- H2O at Scale in a Larger Platform Context
- Appendix – Alternative Methods to Launch H2O Clusters
"Synopsis" may belong to another edition of this title.
About the Author
Gregory Keys is a Senior Solution Architect at H2O and has over 20 years of experience designing and implementing software and data systems. He innovated a model deployment and governance framework that was incorporated into Cloudera machine learning product line.
David Whiting is a Data Science Director and Head of Training at H2O.ai. He has over 18 years of experience in business, consulting, and academia. He is adept at developing and maintaining long-term collaborations with experts in multiple fields. He has both led and participated in multi-disciplinary teams and he enjoys mentoring developing analysts and has a substantial experience in doing so.
"About the title" may belong to another edition of this title.
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Engberdingdamm 27
Greven, Germany 48268
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Instructions for revocation
Revocation right
You have the right to revoke this contract within one month without specifying any reasons.
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In order to exercise your revocation right, you must inform us (Moluna GmbH, Engberdingdamm 27, 48268 Greven, Telephone number: 02571/5 69 89 33, Fax number: 02571/5 69 89 30, E-Mail address: abe@moluna.de) of your decision to revoke this contract by means of a clear declaration (e.g. a letter sent via post, fax or email). You can use the enclosed specimen revocation form for this, which however is not mandatory.
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Specimen - revocation form
(If you wish to revoke the contract, please fill up this form and send it back to us.)
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To Moluna GmbH, Münsterstr. 105, 48268 Greven, Fax number: 02571/5 69 89 30, Email address: abe@moluna.de :
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I/we () herewith revoke the contract concluded by me/ us () regarding the purchase of the following products ()/
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c) The warranty period shall be one year from delivery of the goods. The shortening of the period shall not apply
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II. Customer information
- Identity of the seller
Moluna GmbH
Engberdingdamm 27
48268 Greven
Germany
Telephone: 02571/5698933
E-Mail: abe@moluna.de
- Information regarding the conclusion of the contract
The technical steps associated with the conclusion of the contract, the contract conclusion itself and the correction options are executed in accordance to the regulations "conclusion of the contract" in our standard business terms (part I.).
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If you are a businessman, the delivery and shipping operations take place at your own risk.
- Statutory warranty right
Liability for defects is governed by the “Warranty” provisions in our General Terms and Conditions of Business (Part I).
last update: 01.01.2022
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last update: 10.01.2022