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

Summary

We began this chapter by taking a high-level view of the transition from model building to model deployment. We saw that this transition is bridged for H2O by the MOJO, a deployable representation of the trained model that is easy to generate from model building and easy to deploy for fast model scoring.

We then took a closer look at the range of target systems MOJOs can be deployed on, and saw that these must run in a Java runtime but, otherwise, are quite diverse. MOJOs can be scored on real-time, batch, and streaming systems, usefully categorized as H2O Scorers (scoring software provided and supported by H2O), third-party integrations (software provided and supported by companies other than H2O), and your software integrations (software that you build and maintain).

This categorization of target systems helps us determine whether you can deploy the exported MOJO directly, or whether you need to wrap it in a Java class using the h2o-genmodel API to embed it into the...