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Automated Machine Learning

Automated Machine Learning

By : Adnan Masood
4.5 (15)
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Automated Machine Learning

Automated Machine Learning

4.5 (15)
By: Adnan Masood

Overview of this book

Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks.
Table of Contents (15 chapters)
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1
Section 1: Introduction to Automated Machine Learning
5
Section 2: AutoML with Cloud Platforms
12
Section 3: Applied Automated Machine Learning

Chapter 3: Automated Machine Learning with Open Source Tools and Libraries

"Empowerment of individuals is a key part of what makes open source work since, in the end, innovations tend to come from small groups, not from large, structured efforts."

– Tim O'Reilly

"In open source, we feel strongly that to really do something well, you have to get a lot of people involved."

– Linus Torvalds

In the previous chapter, you looked under the hood of automated Machine Learning (ML) technologies, techniques, and tools. You learned how AutoML actually works – that is, the algorithms and techniques of automated feature engineering, automated model and hyperparameter turning, and automated deep learning. You also explored Bayesian optimization, reinforcement learning, the evolutionary algorithm, and various gradient-based approaches by looking at their use in automated ML.

However, as a hands-on engineer, you probably don't get the...

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