Book Image

Automated Machine Learning

By : Adnan Masood
Book Image

Automated Machine Learning

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)
1
Section 1: Introduction to Automated Machine Learning
5
Section 2: AutoML with Cloud Platforms
12
Section 3: Applied Automated Machine Learning

Chapter 7: Doing Automated Machine Learning with Amazon SageMaker Autopilot

"One of the holy grails of machine learning is to automate more and more of the feature engineering process."

– Pedro Domingos

"Automated machine learning, the best thing since sliced bread!"

– Anonymous

Automated Machine Learning (AutoML) via hyperscalers – that is, via cloud providers – has the potential to bring AI democratization to the masses. In the previous chapter, you created a Machine Learning (ML) workflow in SageMaker, and also learned about the internals of SageMaker Autopilot.

In this chapter, we will look at a couple of examples explaining how Amazon SageMaker Autopilot can be used in a visual, as well as in notebook, format.

In this chapter, we will cover the following topics:

  • Creating an Amazon SageMaker Autopilot limited experiment
  • Creating an AutoML experiment
  • Running the SageMaker Autopilot experiment and...