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Practical Automated Machine Learning Using H2O.ai

You're reading from Practical Automated Machine Learning Using H2O.ai Discover the power of automated machine learning, from experimentation through to deployment to production

Product type Paperback

Published in Sep 2022

Publisher Packt

ISBN-13 9781801074520

Length 396 pages

Edition 1st Edition

Tools

H2O

Concepts

Machine Learning

Author (1):

Salil Ajgaonkar

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1 H2O AutoML Basics

2. Chapter 1: Understanding H2O AutoML Basics FREE CHAPTER

3. Chapter 2: Working with H2O Flow (H2O’s Web UI)

4. Part 2 H2O AutoML Deep Dive

5. Chapter 3: Understanding Data Processing

6. Chapter 4: Understanding H2O AutoML Architecture and Training

7. Chapter 5: Understanding AutoML Algorithms

8. Chapter 6: Understanding H2O AutoML Leaderboard and Other Performance Metrics

9. Chapter 7: Working with Model Explainability

10. Part 3 H2O AutoML Advanced Implementation and Productization

11. Chapter 8: Exploring Optional Parameters for H2O AutoML

12. Chapter 9: Exploring Miscellaneous Features in H2O AutoML

13. Chapter 10: Working with Plain Old Java Objects (POJOs)

14. Chapter 11: Working with Model Object, Optimized (MOJO)

15. Chapter 12: Working with H2O AutoML and Apache Spark

16. Chapter 13: Using H2O AutoML with Other Technologies

17. Index

Why subscribe?

18. Other Books You May Enjoy

Technical requirements

For this chapter, you will require the following:

The latest version of your preferred web browser.
An Integrated Development Environment (IDE) of your choice or a Terminal.
All the experiments conducted in this chapter are performed on a Terminal. You are free to follow along using the same setup or you can perform the same experiments using any IDE of your choice.

All the code examples for this chapter can be found on GitHub at https://github.com/PacktPublishing/Practical-Automated-Machine-Learning-on-H2O/tree/main/Chapter%207.

So, let’s begin by understanding how the model explainability interface works.

The rest of the chapter is locked

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Authors (1)

Salil Ajgaonkar

Salil Ajgaonkar

Salil Ajgaonkar is a software engineer experienced in building and scaling cloud-based microservices and productizing machine learning models. His background includes work in transaction systems, artificial intelligence, and cyber security. He is passionate about solving complex scaling problems, building machine learning pipelines, and data engineering. Salil earned his degree in IT from Xavier Institute of Engineering, Mumbai, India, in 2015 and later earned his master's degree in computer science from Trinity College Dublin, Ireland, in 2018, specializing in future networked systems. His work history includes the likes of BookMyShow, Genesys, and Vectra AI.

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