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

Model building and evaluation

Our approach to model building starts with AutoML. Global explainability applied to the AutoML leaderboard either results in picking a candidate model or yields insights that we feed back into a new round of modified AutoML models. This process can be repeated if improvements in modeling or explainability are apparent. If a single model rather than a stacked ensemble is chosen, we can show how an additional random grid search could produce better models. Then, the final candidate model is evaluated.

The beauty of this approach in H2O-3 is that the modeling heavy lifting is done for us automatically with AutoML. Iterating through this process is straightforward, and the improvement cycle can be repeated, as needed, until we have arrived at a satisfactory final model.

We organize the modeling steps as follows:

  1. Model search and optimization with AutoML.
  2. Investigate global explainability with the AutoML leaderboard models.
  3. Select a...