Machine Learning at Scale with H2O

"Machine Learning at Scale with H2O" is a comprehensive guide that demonstrates how to leverage the powerful H2O framework to build and deploy machine learning models effectively in large-scale enterprise environments. You will explore key topics like in-memory distributed architectures, integrating H2O with Spark, model deployment using MOJO, and utilizing the H2O AI Cloud for advanced workflows.

What this Book will help me do

• Understand and utilize H2O's scalable architecture for building large-scale machine learning models.
• Learn methods to deploy models for various production environments including batch, real-time, and streaming scenarios.
• Integrate H2O operations with Apache Spark efficiently via H2O Sparkling Water.
• Discover techniques to ensure model interpretability using the H2O framework.
• Master the use of the H2O AI Cloud for end-to-end enterprise machine learning workflows.

Author(s)

Gregory Keys is an experienced data scientist with over 10 years in the field specializing in machine learning applications in enterprise settings. David Whiting is a senior software architect with deep expertise in distributed systems and AI cloud platforms. Together, they share their practical insights and expertise, guiding readers to achieve measurable success through advanced machine learning techniques.

Who is it for?

This book is designed for data scientists and machine learning engineers looking to enhance their skills in building and deploying scalable ML solutions. It provides valuable insights for those with foundational data science knowledge aiming to apply ML effectively in enterprise-level scenarios. Python experience is recommended, making this book ideal for professionals seeking to innovate and drive business value through machine learning applications.