Machine Learning at Scale with H2O

By Gregory Keys , David Whiting
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About this book

H2O is an open-source, fast, and scalable machine learning and predictive analytics platform that allows you to build machine learning models using big data and provides easy productionalization of those models in an enterprise environment.

Machine Learning at Scale with H2O starts with the challenges of building machine learning models at scale on enterprise systems and how the H2O platform solves those challenges. You’ll then cover the seamless integration of machine learning models with Spark coding and DataFrames using H2O Sparkling Water and understand how to easily deploy models with H2O MOJO. Later, the book explores the in-memory distributed compute of your favorite ML algorithms with the help of H2O-3 with Flow. You’ll also cover the implementation of H2O Enterprise Steam for admin configurations and user management. Next, you’ll discover the different stakeholders and their technical needs that a data scientist must understand in order to build and deploy models successfully. Finally, you’ll focus on the H2O AI Hybrid Cloud platform, exploring the full machine learning lifecycle and AI capabilities of the platform.

By the end of this machine learning book, you’ll have learned how to build advanced, state-of-the-art models for your business needs.

Publication date:
September 2022
Publisher
Packt
Pages
333
ISBN
9781800566019

About the Authors

  • Gregory Keys

    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.

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  • David Whiting

    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.

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Machine Learning at Scale with H2O
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