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

Leveraging H2O Flow to enhance your IDE workflow

H2O Flow is a web-based UI available wherever an H2O cluster is running. Flow is interactive, allowing users to do everything including import data, build models, investigate models, and put models into production. While incredibly easy to use, our experience is that most data scientists (authors included) prefer coding in Python or R to menu-driven interactive interfaces. This section is written for those data scientists: why use Flow when I am a coder?

There are two main reasons:

  • Monitoring the state of the H2O cluster and the jobs that are being run
  • Interactive investigation of the data, models, model diagnostics, and more where interactivity is an asset rather than an annoyance

Connecting to Flow

By default, Flow is started on port 54321 of the H2O server as the cluster is launched (this port is configurable at startup). Enter Error! Hyperlink reference not valid. into your browser to open Flow. The Flow...