Machine Learning at Scale with H2O
By :
Machine Learning at Scale with H2O
By:
Overview of this book
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.
By the end of this book, you’ll be able to build and deploy advanced, state-of-the-art machine learning models for your business needs.
Table of Contents (22 chapters)
Preface
Section 1 – Introduction to the H2O Machine Learning Platform for Data at Scale
Free Chapter
Chapter 1: Opportunities and Challenges
Chapter 2: Platform Components and Key Concepts
Chapter 3: Fundamental Workflow – Data to Deployable Model
Section 2 – Building State-of-the-Art Models on Large Data Volumes Using H2O
Chapter 4: H2O Model Building at Scale – Capability Articulation
Chapter 5: Advanced Model Building – Part I
Chapter 6: Advanced Model Building – Part II
Chapter 7: Understanding ML Models
Chapter 8: Putting It All Together
Section 3 – Deploying Your Models to Production Environments
Chapter 9: Production Scoring and the H2O MOJO
Chapter 10: H2O Model Deployment Patterns
Section 4 – Enterprise Stakeholder Perspectives
Chapter 11: The Administrator and Operations Views
Chapter 12: The Enterprise Architect and Security Views
Section 5 – Broadening the View – Data to AI Applications with the H2O AI Cloud Platform
Chapter 13: Introducing H2O AI Cloud
Chapter 14: H2O at Scale in a Larger Platform Context
Other Books You May Enjoy
Appendix : Alternative Methods to Launch H2O Clusters
Customer Reviews