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

In this chapter, we reviewed the entire data science model-building process. We started with raw data and a somewhat vaguely defined use case. Further inspection of the data allowed us to refine the problem statement to one that was relevant to the business and that could be addressed with the data at hand. We performed extensive feature engineering in the hopes that some features might be important predictors in our model. We introduced an efficient and powerful method of model building using H2O AutoML to build an array of different models using multiple algorithms. Selecting one of those models, we demonstrated how to further refine the model with additional hyperparameter tuning using grid search. Throughout the model-building process, we used the diagnostics and model explanations introduced in Chapter 7, Understanding ML Models, to evaluate our ML model. After arriving at a suitable model, we showed the simple steps required to prepare for the enterprise deployment of...