Practical Automated Machine Learning Using H2O.ai

Accelerate the adoption of ML by automating away the complex parts of the ML pipeline using H2O.ai

Key Features

• Learn how to train the best models with a single click using H2O AutoML
• Get simple explanation of model performance using H2O Explainability
• Easily deploy your trained models to production using H2O MOJO and POJO

Book Description

Machine Learning has come a long way from being in academic studies to adoption by major tech companies. In this day and age, with the amount of data that is generated over the internet and the benefits that ML predictions can provide a business, machine learning 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 what H2O tries to hide away by automating it. This encapsulation helps developers focus on results rather than handling complexities.

H2O's AutoML simplifies the implementation of machine learning by providing a simple easy-to-use interface to train and use machine learning models. It automates the whole process of training multiple models, optimizing their hyperparameters as well as explaining their performance. This book provides a hands-on approach to implementation using H2O that will help you set up your ML systems in no time.

By the end of this book, you will be able to train and use your machine learning models using H2O AutoML right from experimentation all the way to production without a single need to learn or understand complex statistics or data-science.

What you will learn

• Learn how to use H2O AutoML
• Explore the H2O Flow Web UI
• Understand how H2O AutoML trains the best models
• Understand how H2O AutoML automates hyperparameter optimization
• Discover how H2O Explainability helps understand model performance
• Explore H2O integration with Scikit-Learn, Spring and Apache Storm
• Learn how to use H2O with Spark using H2O-Sparkling Water
• Learn how to deploy your models to production using H2O MOJO and POJO

Who This Book Is For

This book is for engineers and data scientists that want to quickly adopt machine learning into their products without worrying about the internal intricacies of training machine learning models. If you are someone who wants to incorporate machine learning into your software system but don't know where to start or don't have much expertise in the domain of ML then you will find this book useful. Basic knowledge of statistics and programming is beneficial. Some understanding of ML and Python will be helpful.

Table of Contents

1. Understanding H2O AutoML Basics
2. Working with H2O Flow (H2O's Web UI)
3. Understanding Data Processing
4. Understanding H2O AutoML Training and Architecture
5. Understanding AutoML Algorithms
6. Understanding H2O AutoML Leaderboard and Other Performance Metrics
7. Working with Model Explainability
8. Exploring Optional Parameters for H2O AutoML
9. Exploring Miscellaneous Features in H2O AutoML
10. Working with Plain Old Java Objects (POJOs)
11. Working with Model Object, Optimized (MOJO)
12. Working with H2O AutoML and Apache Spark
13. Using H2O AutoML with Other Technologies