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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 6: Machine Learning with AWS

"Whatever you are studying right now, if you are not getting up to speed on deep learning, neural networks, etc., you lose. We are going through the process where software will automate software, automation will automate automation."

– Mark Cuban

In the previous chapter, you were introduced to the Azure Machine Learning (ML) landscape and how to do automated ML in the Azure platform. In this chapter, you will learn how to get started with ML using Amazon Web Services (AWS), along with different offerings and a detailed understanding of the ginormous AWS cloud stack.

The theme of this chapter is to get started with an introduction to AWS ML capabilities to give a wider perspective of this large ecosystem; not only AWS as a hyperscaler but also the breadth of the field itself. Many use cases and permutations require specialized solutions and there is no one-size-fits-all solution for an enterprise's AI and ML needs...

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