Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

Adnan Masood

$19.99 per month

4.5 (15 Ratings)

Paperback Feb 2021 312 pages 1st Edition

Adnan Masood

$19.99 per month

4.5 (15 Ratings)

Paperback Feb 2021 312 pages 1st Edition

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Key benefits

Get up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choice
Eliminate mundane tasks in data engineering and reduce human errors in machine learning models
Find out how you can make machine learning accessible for all users to promote decentralized processes

Description

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.

Who is this book for?

Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.

What you will learn

Explore AutoML fundamentals, underlying methods, and techniques
Assess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenario
Find out the difference between cloud and operations support systems (OSS)
Implement AutoML in enterprise cloud to deploy ML models and pipelines
Build explainable AutoML pipelines with transparency
Understand automated feature engineering and time series forecasting
Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems

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MLEngineer Mar 29, 2021

This book contains a nice overview of the 'big-picture' of Machine Learning. Machine Learning pipelines involve a lot of steps and after iterating on a few models at production every ML Professional should look at the big machine learning picture. The book talks about data gathering, hyper-parameter optimization, working on various clouds, and never forgetting about the domain/business/production of each ML model.This is a very nice book.

Amazon Verified review

Lucinda Linde Mar 20, 2021

Disclaimer: This review has been requested by the publisher, and I am giving my honest review of this book. This review is based on reading the book. As with any Packt publication, it's also necessary to try out the code, which I will at a later point in time.OverviewAutomated Machine Learning is a very helpful primer on the up-to-date options available to use automated machine learning. This book is helpful to someone who has built ML models and wants to automate some of the more repetitive parts. I am looking into taking the AWS Cloud Practitioner exam. This will help me understand some of the AWS cloud offerings.What I like about this book:The book starts by giving a framework by which to compare the different auto-ML options. Machine Learning techniques have evolved to have a mind-numbing number of parameters to tune. To help data scientists optimize and scale building model, automation has become more important to realizing the benefits of these new models. The main three things to automate are Feature Engineering, Hyperparameter and model selection, and Deep Learning. I like that the author keeps this framework in the reader's mind each time the material is covered in increasing depth.The book starts with the big picture in Chapters 1-3, showing the open source and proprietary options for auto-ML. It's great to learn that there are free and open-source options to automate machine learning. There are entire chapters and parts of the book devoted to Google Colab, Linux, TPOT and other free versions.Then there are multiple chapter deep dives on the major environments of ML: Microsoft Azure, Amazon Web Services (AWS) and Google Cloud. For each of these topics, very helpful screen shots are provided to:• Setup the environment, account; install initial libraries• Supply code to for example projects so that the reader can practice using auto-ML• Show what the ouput looks like, and explain the outputThe visual frameworks, process flow diagrams, tables etc. put order to the inherent complexity, and provide a useful way comparing the major options (MS Azure, AWS and Google). Just looking at the structure of the tables brings out what’s important and the contents of the tables highlight what’s different.Finally, I love the nerd humor that occasionally pops up. Makes for a fun reading experience.One worry about this bookThe screen shots are very helpful to set up the programs and examples especially right when this is published. One worry is that those screen shots will be out of date in a few months. These differences may cause confusion as readers try to implement these examples.Overall, " Automated Machine Learning" is a really helpful introduction with some hands-on initial examples into the options to use when automating complex machine learning models. Auto-ML automates the repetitive and sprawling tasks to building machine learning models with lots of features and parameters.

Adwait Ullal Aug 29, 2021

Note: I was provided with a free eBook copy in exchange for an unbiased review.This book, Automated Machine Learning, provides a succinct summary on the state of AutoML and the enterprise.The book is divided into three sections:- Introduction to AutoML- State of AutoML in the cloud(s)- AutoML in the EnterpriseThe topics in the book are organized well for anyone who wants to understand AutoML.What I liked about:- Depending on your skill level, you can choose a Section of interest (i.e. an experienced ML developer can jump directly to the cloud section)- Logical progression- Coverage of the top three cloud providers (AWS, Azure & GCP)Room for improvement:- AutoML in the Enterprise could use some more organization and/or topics in terms of smaller chapters, roadmaps, etc.In summary, this is a good book to quick understanding of AutoML across the top three cloud platforms.

Julie Zhu Apr 19, 2021

As a business owner and building machine learning models, the goal is to implement the models into production automation. This book has provided a full stack of implementations including the well known AutoML tools such as Microsoft Azure, AWS and Google Cloud step by step, very instrumental and practical book to follow. I would strongly recommend this highly sought book.

David G Mar 23, 2021

Automated machine learning is a highly sought after skill in modern ML development stack, and it is quickly becoming part of all modern AI platforms. I wanted a breadth first approach of the topic, with an overview of different cloud AutoML technologies. This book provides exactly the right amount of breadth and depth into multiple cloud platforms, and open source toolkits. Highly recommended.

Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

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