Book Image

Machine Learning at Scale with H2O

By : Gregory Keys, David Whiting
Book Image

Machine Learning at Scale with H2O

By: Gregory Keys, David Whiting

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)
1
Section 1 – Introduction to the H2O Machine Learning Platform for Data at Scale
5
Section 2 – Building State-of-the-Art Models on Large Data Volumes Using H2O
11
Section 3 – Deploying Your Models to Production Environments
14
Section 4 – Enterprise Stakeholder Perspectives
17
Section 5 – Broadening the View – Data to AI Applications with the H2O AI Cloud Platform

Chapter 2: Platform Components and Key Concepts

In this chapter, we will gain a fundamental understanding of the components of H2O's machine learning at scale technology. We will view a simple code example of H2O machine learning, understand what it does, and identify any problems the example has with machine learning at an enterprise scale. This Hello World code example will serve as a simple representation in which to build our understanding further.

We will overview each H2O component of machine learning at scale, identify how each component achieves scale, and identify how each component relates to our simple code snippet. Then, we will tie these components together into a reference machine learning workflow using these components. Finally, we will focus on the underlying key concepts that arise from these components. The understanding obtained in this chapter will be foundational to the rest of the book, where we will be implementing H2O technology to build and deploy...