Machine Learning at Scale with H2O

Build predictive models using large data volumes and deploy them to production using cutting-edge techniques

Key Features

• Get a better understanding of your data science problems and how H2O can help solve them
• Build highly accurate state-of-the-art machine learning models against data at a large scale
• Become well-versed with efficiently deploying your models in production systems

Book Description

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.

What you will learn

• Leverage H2O platform components to build and deploy ML models
• Launch self-service environments and IDEs against data with H2O Enterprise Steam
• Explore the flexibility of H2O MOJO's scoring artifact in live environments
• Open machine learning lifecycle and AI possibilities with the H2O AI Hybrid Cloud platform
• Integrate Spark data engineering in an ML model with H2O Sparkling Water
• Use H2O AutoML with interpretability and auto-documentation for your models

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 state-of-the-art models using H2O technology. Basic knowledge of R and Python programming as well as the H2O platform is necessary. You also need to have a practical understanding of the data science process before getting started.

Table of Contents

1. Opportunities and Challenges
2. Platform Components and Key Concepts
3. Typical Workflow: Data to Mojo
4. Breaking It Down
5. Advanced Model Building Topics, Part I
6. Advanced Model Building Topics, Part II
7. Understanding your Models
8. Putting it All Together
9. Understanding the MOJO
10. Deployment Recipes
11. The Administrator View
12. The Enterprise Architect and Security View
13. The Operations View
14. The Governance View
15. The Business View
16. The Machine Learning Lifecycle, AI Apps and H2O AI Hybrid Cloud
17. Back to H2O-3, Sparkling Water and Enterprise Steam