Machine Learning at Scale with H2O
eBook Details:
- Paperback: 396 pages
- Publisher: WOW! eBook (July 29, 2022)
- Language: English
- ISBN-10: 1800566018
- ISBN-13: 978-1800566019
eBook Description:
Machine Learning at Scale with H2O: Build predictive models using large data volumes and deploy them to production using cutting-edge techniques
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.
- 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
By the end of this Machine Learning at Scale with H2O book, you’ll be able to build and deploy advanced, state-of-the-art machine learning models for your business needs.
