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

Summary

In this chapter, we covered the ML development life cycle and then defined automated ML and how it works. While building a case for the need for automated ML, we discussed the democratization of data science, debunked the myths surrounding automated ML, and provided a detailed walk-through of the automated ML ecosystem. Here, we reviewed the open source tools and then explored the commercial landscape. Finally, we discussed the future of automated ML, commented on the challenges and limitations of it, and finally provided some pointers on how to get started in an enterprise.

In the next chapter, we'll look under the hood of the technologies, techniques, and tools that are used to make automated ML possible. We hope that this chapter has introduced you to the automated ML fundamentals and that you are now ready to do a deeper dive into the topics that we discussed.