Machine Learning at Scale with H2O

Table of Contents

Preface

Section 1 – Introduction to the H2O Machine Learning Platform for Data at Scale

Chapter 1: Opportunities and Challenges

ML at scale

The ML life cycle and three challenge areas for ML at scale

A simplified ML life cycle

The model building challenge – state-of-the-art models at scale

The business challenge – getting your models into enterprise production systems

The navigation challenge – navigating the enterprise stakeholder landscape

H2O.ai's answer to these challenges

Summary

Chapter 2: Platform Components and Key Concepts

Technical requirements

Hello World – the H2O machine learning code

Code example

Some issues of scale

The components of H2O machine learning at scale

H2O Core – in-memory distributed model building ...