Book Image

Practical Automated Machine Learning Using H2O.ai

By : Salil Ajgaonkar
Book Image

Practical Automated Machine Learning Using H2O.ai

By: Salil Ajgaonkar

Overview of this book

With the huge amount of data being generated over the internet and the benefits that Machine Learning (ML) predictions bring to businesses, ML implementation has become a low-hanging fruit that everyone is striving for. The complex mathematics behind it, however, can be discouraging for a lot of users. This is where H2O comes in – it automates various repetitive steps, and this encapsulation helps developers focus on results rather than handling complexities. You’ll begin by understanding how H2O’s AutoML simplifies the implementation of ML by providing a simple, easy-to-use interface to train and use ML models. Next, you’ll see how AutoML automates the entire process of training multiple models, optimizing their hyperparameters, as well as explaining their performance. As you advance, you’ll find out how to leverage a Plain Old Java Object (POJO) and Model Object, Optimized (MOJO) to deploy your models to production. Throughout this book, you’ll take a hands-on approach to implementation using H2O that’ll enable you to set up your ML systems in no time. By the end of this H2O book, you’ll be able to train and use your ML models using H2O AutoML, right from experimentation all the way to production without a single need to understand complex statistics or data science.
Table of Contents (19 chapters)
1
Part 1 H2O AutoML Basics
4
Part 2 H2O AutoML Deep Dive
10
Part 3 H2O AutoML Advanced Implementation and Productization

Technical requirements

You will require the following to complete this chapter:

  • The latest version of your preferred web browser.
  • The H2O software installed on your system. Refer to Chapter 1, Understanding H2O AutoML Basics, for instructions on how to install H2O on your system.

Tip

All the H2O AutoML function parameters shown in this chapter are shown using H2O Flow to keep things simple. The equivalent parameters are also available in the Python and R programming languages for software engineers to code into their services. You can find these details at https://docs.h2o.ai/h2o/latest-stable/h2o-docs/parameters.html.