Gregory Keys - Machine Learning at Scale with H2O: A practical guide to building and deploying machine learning models on enterprise systems
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- Book:Machine Learning at Scale with H2O: A practical guide to building and deploying machine learning models on enterprise systems
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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
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. Youll start by exploring H2Os 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. Youll also get to grips with the seamless integration of H2O model building and deployment with Spark using H2O Sparkling Water. Youll 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, youll 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, youll 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
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
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A practical guide to building and deploying machine learning models on enterprise systems
Gregory Keys
David Whiting
BIRMINGHAMMUMBAI
Copyright 2022 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Publishing Product Manager: Aditi Gour
Senior Editor: David Sugarman
Content Development Editor: Manikandan Kurup
Technical Editor: Rahul Limbachiya
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First published: July 2022
Production reference: 1290622
Published by Packt Publishing Ltd.
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ISBN 978-1-80056-601-9
www.packt.com
My deepest love and warmth to Mary, Julia and Alexa for their support and understanding while husband and dad disappeared to the basement for significant chunks of nights and weekends as the seasons progressed.
- Gregory
To my wife Kathy, and son Ben, who endured too many late nights and weekends of dad locked away in his study working; the book has been a family effort and its culmination a family success.
- David
This book would not have been possible without the approval and support of our respective leaders at H2O.ai at the time of its writing, Dmitry Baev and Eyal Kaldes. In addition, we pay our great appreciation to the deep expertise of the many Makers at H2O.ai. Their day-to-day collaboration, education, and machine learning expertise are diffused throughout the pages of this book.
One name needs to be called out in particular: massive thanks to Eric Gudgeon for his infinite and unrelenting technical teachings, and for defining and developing a vast landscape of H2O model deployment implementations.
This book took longer to pull together than either of us expected. Working at a hyper-focused and highly energized company certainly was a contributing factor. Against this backdrop, we appreciate the world-class patience, encouragement, guidance, and professionalism of the Packt team in collaborating on this book from start to finish.
And most importantly there is family, who unfairly signed up for book writing without fully knowing it.
Gregory Keys is a master principal cloud architect for Data and AI at Oracle. Formerly a senior solutions architect at H2O.ai, he has over 20 years of experience designing and implementing software and data systems. He specializes in AI/ML solutions and has multiple software patents. Gregory has a PhD in evolutionary biology, which has greatly influenced him as a systems thinker.
David Whiting is a data science director and head of training at H2O.ai. He has a PhD in statistics from Texas A&M University and over 25 years of professional experience in academia, consulting, and industry. He has built and led data science teams in financial services and other regulated enterprises.
Jan Gamec is a lead software engineer at H2O.ai and one of the top contributors to a state-of-the-art AutoML platform called Driverless AI. In the past decade, he has contributed to various projects, focusing on machine learning, cryptography, and web technologies, either in the public or academic sector. Jan holds a master's degree in machine learning and computer science from CTU, Czech Republic, with the main focus of interest being genetic programming, neural networks, and reinforcement learning.
Jagadeesh Rajarajan has over 10 years of experience in building scalable data science systems. He has rich domain knowledge in the following areas: search relevance (information retrieval), recommender systems, AI for customer engagement (acquisition, activation, and retention), MLOps, and interpretable machine learning systems.
Eric Gudgeon has worked on many large complex systems, built nationwide networks, and helped customers deploy highly scalable low-latency solutions. He has a passion for technology and finding creative solutions to problems.
Ondrej Bilek is a lead software engineer at H2O.ai and has rich experience designing and implementing machine learning platforms for Hadoop and Kubernetes. He led the development of Enterprise Steam and is currently working on the H2O AI Cloud.
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