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

Understanding H2O AutoML Basics

Machine Learning (ML) is the process of building analytical or statistical models using computer systems that learn from historical data and identify patterns in them. These systems then use these patterns and try to make predictive decisions that can provide value to businesses and research alike. However, the sophisticated mathematical knowledge required to implement an ML system that can provide any concrete value has discouraged several people from experimenting with it, leaving tons of undiscovered potential that they could have benefited from.

Automated Machine Learning (AutoML) is one of the latest ML technologies that has accelerated the adoption of ML by organizations of all sizes. It is the process of automating all these complex tasks involved in the ML life cycle. AutoML hides away all these complexities and automates them behind the scenes. This allows anyone to easily implement ML without any hassle and focus more on the results.

In this chapter, we will learn about one such AutoML technology by H2O.ai (https://www.h2o.ai/), which is simply named H2O AutoML. We will provide a brief history of AutoML in general and what problems it solves, as well as a bit about H2O.ai and its H2O AutoML technology. Then, we will code a simple ML implementation using H2O’s AutoML technology and build our first ML model.

By the end of this chapter, you will understand what exactly AutoML is, the company H2O.ai, and its technology H2O AutoML. You will also understand what minimum requirements are needed to use H2O AutoML, as well as how easy it is to train your very first ML model using H2O AutoML without having to understand any complex mathematical rocket science.

In this chapter, we are going to cover the following topics:

  • Understanding AutoML and H2O AutoML
  • Minimum system requirements to use H2O AutoML
  • Installing Java
  • Basic implementation of H2O using Python
  • Basic implementation of H2O using R
  • Training your first ML model using H2O AutoML