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

ISBN-13: 978-1800567689, ISBN-10: 1800567685
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Book details

  1. ISBN-10
    1800567685
  2. ISBN-13
    978-1800567689
  3. Publisher
    Packt Publishing
  4. Publication date
    February 18, 2021
  5. Language
    English
  6. Dimensions
    7.5 x 0.71 x 9.25 inches
  7. Print length
    312 pages
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Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies

Key Features

  • 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

Book 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.

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

Who this book is 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.

Table of Contents

  1. A Lap around Automated Machine Learning
  2. Automated Machine Learning, Algorithms, and Techniques
  3. Automated Machine Learning with Open Source Tools and Libraries
  4. Getting Started with Azure Machine Learning
  5. Automated Machine Learning with Microsoft Azure
  6. Machine Learning with Amazon Web Services
  7. Doing Automated Machine Learning with Amazon SageMaker Autopilot
  8. Machine Learning with Google Cloud Platform
  9. Automated Machine Learning with GCP Cloud AutoML
  10. AutoML in the Enterprise

About the Author

Adnan Masood, PhD is an artificial intelligence and machine learning researcher, visiting scholar at Stanford AI Lab, software engineer, Microsoft MVP (Most Valuable Professional), and Microsoft's regional director for artificial intelligence. As chief architect of AI and machine learning at UST Global, he collaborates with Stanford AI Lab and MIT CSAIL, and leads a team of data scientists and engineers building artificial intelligence solutions to produce business value and insights that affect a range of businesses, products, and initiatives.

About the author

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Adnan Masood
Adnan Masood
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Dr. Adnan Masood, a distinguished AI and Machine Learning thought leader, currently serves as the Chief AI Architect at UST. With over two decades of international experience in the development of large-scale AI systems and FinTech his responsibilities encompass the firm's strategic approach towards cognitive computing, artificial intelligence, machine learning, and fostering academic alliances.

Recognized as a Microsoft Regional Director and a Microsoft Most Valuable Professional (MVP) for Artificial Intelligence, Dr. Masood's accolades attest to his significant contributions in the field. His role involves strategic collaborations with Stanford Artificial Intelligence Lab and MIT CSAIL, leading a team of data scientists and engineers in the creation of AI solutions that deliver actionable business insights and value across various businesses, products, and initiatives.

Dr. Masood's expertise has made him a sought-after advisor to C-suite executives, from Fortune 500 corporations to innovative startups. In addition to his advisory role, he imparts his knowledge as an international speaker at various academic and technological conferences, including ISG Summit, Azure+AI Conference, IEEE-HST, IASA, DevConnections, WBA Geekfest, WICT, international code camps, and user groups.

Residing in Florida with his two sons, Dr. Masood's personal pursuits include mastering Fortnite and maintaining a lively blog. His unique perspectives classify Pluto as a planet, categorize chocolate as a food group, and position A Game of Thrones as historical fiction. He dedicates part of his time to volunteer as a STEM robotics coach for elementary and middle school students, promoting diversity in the workplace.

For further details, visit Dr. Masood's blog (blog.AdnanMasood.com), GitHub (https://github.com/adnanmasood/), or Twitter (@adnanmasood). He can be reached at [email protected].

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Product information

Publisher Packt Publishing
Publication date February 18, 2021
Language ‎English
Print length 312 pages
ISBN-10 1800567685
ISBN-13 978-1800567689
Item Weight ‎1.19 pounds
Dimensions 7.5 x 0.71 x 9.25 inches
Best Sellers Rank
Customer Reviews 4.4 out of 5 stars 19Reviews

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Top reviews from the United States

  • Amazon Customer
    5.0 out of 5 starsVerified Purchase
    Important coverage of AutoML in cloud platforms
    Reviewed in the United States on March 1, 2021
    Format: Paperback
    As an AI enthusiast, I like that this manuscript provides the breadth of knowledge around the emerging field of automated machine learning. The coverage of multiple clouds in particular helps the reader to understand the state of the eco-system, along with its open source...
    As an AI enthusiast, I like that this manuscript provides the breadth of knowledge around the emerging field of automated machine learning. The coverage of multiple clouds in particular helps the reader to understand the state of the eco-system, along with its open source components. We quickly see AI platforms adopting AutoML, and it is timely that practitioners learn about this technology. Highly recommended.
    3 people found this helpful
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  • Jagannath Banerjee
    4.0 out of 5 stars
    Extensive Coverage of Open Source & Commercial Automated Machine Learning
    Reviewed in the United States on March 23, 2021
    Format: Paperback
    Automated Machine Learning (AutoML) is recommended for beginners having basic familiarity with Python, Cloud environments (Like Azure, AWS & Google Cloud) , Machine Learning, Image Processing and Time Series. This book does not dive into basics of the above concepts...
    Automated Machine Learning (AutoML) is recommended for beginners having basic familiarity with Python, Cloud environments (Like Azure, AWS & Google Cloud) , Machine Learning, Image Processing and Time Series. This book does not dive into basics of the above concepts but covers automated machine learning and its implementation with open source and commercially available sources in an organized fashion with details.

    Book gives a beautiful background of automated machine learning, when to use it, when not to use it, scope, advantages and impact of using AutoML.

    First part of the book covers the popular open source automated machine learning system using TPOT, AutoKeras, auto-sklearn, Featuretools, and Microsoft NNI. Author provides a comparison of the open source systems and its coverage across machine learning life-cycle. Also installation of libraries and implementation of the tools with example works as mentioned.

    Next part of the book covers the commercially available automated machine learning by Azure, AWS and Google Cloud. I was able to focus on Azure AutoML due to my familiarity.

    What stood out for me is the approach in which the book was written. Author explained technical requirements and implementations with multiple examples which worked as mentioned. It will be offer a confident start to beginner data scientists.

    Books mainly covers examples with basic machine learning toy datasets like energy usage prediction, MNIST and NY taxi prediction. I would have liked to see difficult use cases like imbalanced classifiers, anomaly detection etc.
    One person found this helpful
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  • Late Learner
    2.0 out of 5 starsVerified Purchase
    If you like screenshots with tiny white letters against a murky background, you’ll love this book.
    Reviewed in the United States on February 7, 2022
    Format: Paperback
    I applaud the author for covering such a breadth of material relevant to automated machine learning. I will keep the book for the instructional value of the main text alone. However, I cannot understand the overuse of nearly unreadable screenshots throughout the book. The...
    I applaud the author for covering such a breadth of material relevant to automated machine learning. I will keep the book for the instructional value of the main text alone. However, I cannot understand the overuse of nearly unreadable screenshots throughout the book. The screenshots which depict white screens with black text are just fine. Why not choose that format for ALL the screenshots?
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  • Lucinda Linde
    5.0 out of 5 stars
    Very organized tour of Automated Machine Learning; and deep dives into Azure, AWS and Google Cloud
    Reviewed in the United States on March 20, 2021
    Format: Kindle
    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....
    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.

    Overview

    Automated 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 output

    The 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 book

    The 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.
    3 people found this helpful
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  • Amazon Customer
    4.0 out of 5 stars
    Good Coverage But Lacking Some Depth
    Reviewed in the United States on July 31, 2021
    Format: Paperback
    This fluently written book provides a broad coverage of AutoML, concentrating on the current offerings in this field—in particular those of the three large cloud platforms (Azure, AWS and GCP)—but also of several open-source frameworks. In addition, it looks into some...
    This fluently written book provides a broad coverage of AutoML, concentrating on the current offerings in this field—in particular those of the three large cloud platforms (Azure, AWS and GCP)—but also of several open-source frameworks.
    In addition, it looks into some aspects of applying AutoML in the enterprise.
    While this book can be a useful resource to anyone who wants to get started with AutoML, I feel that it falls short when it attempts to go ‘under the hood’ and explain the techniques behind it. Despite offering useful links, the descriptions provided are brief and often contain references to unexplained technical terms. Expanding on this area would be beneficial to the reader who wants to have a deeper understanding of AutoML.
    One person found this helpful
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  • Joe Brown
    5.0 out of 5 stars
    Excellent introdction to Automated Machine Learning in Cloud Platforms
    Reviewed in the United States on April 17, 2021
    Format: Paperback
    Disclaimer: The publisher reached out to me to review this book and has given me a review copy to do so. However, I promise to be 100% honest and completely forthcoming in my review of this book. In addition I am not obliged to review this book, I am simply doing so because...
    Disclaimer: The publisher reached out to me to review this book and has given me a review copy to do so. However, I promise to be 100% honest and completely forthcoming in my review of this book. In addition I am not obliged to review this book, I am simply doing so because I believed this book left enough of an impact for me to leave a review for it.

    Overview: This book's goal is to showcase how to automate common machine learning tasks as well as how to perform these tasks in a cloud environment such as: Microsoft Azure, Google Cloud Services, and Amazing Web Services. Because of this,I would recommend that this book be read by someone who has some experience with AI/ML as well as some knowledge of a cloud service platform.

    What I liked: I enjoyed how the book broke down the benefits of Automating ML tasks, as well as some scenarios as to when you should and shouldn't automate them and it’s uses. The screenshot and provided code examples are very clean and relatively simple. This is good because these are arguably the most important parts of this book when it comes to understanding the material. The diagrams also brought much needed organization & clarity to some of the more complex concepts.

    I also liked how when it came to the cloud sections, that there were various chapters giving a deep dive into how to set-up and actually run/deploy code in these environments. Speaking of the code, I was very happy that the choice of tools showcased and taught were open source and popular (i.e. auto-sklearn and AutoKeras).

    What I didn’t like: Honestly, there wasn’t a lot that I didn’t like. If I had to nitpick there is only one thing that come to mind, but are honestly minor. I believe some sections assume the reader knows certain things without the user being 100% aware. Granted, the parts that aren't explained aren't super important to you understanding the information, but would still be good to know (i.e. giving a brief description of Docker in the MS Azure section).

    Final thoughts:
    This isn't a book for someone who is brand new into the ML space. Regarding the cloud experience I alluded to earlier, I recommend that you have some knowledge of at least one cloud platform before starting this book. While it isn't completely necessary, it will make those sections easier to understand.

    With that being said, if you have the above pre-requisites, then book does an excellent job introducing someone into the world of Automated Machine Learning. I would definitely recommend.
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  • TC H.
    4.0 out of 5 stars
    Best AutoML intro book!
    Reviewed in the United States on April 23, 2021
    Format: Paperback
    This book covers all major techniques for AutoML and then go on covering the mainstream cloud 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...
    This book covers all major techniques for AutoML and then go on covering the mainstream cloud 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 book for whoever want to get jump started with AutoML.
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  • Jeffrey Chilberto
    5.0 out of 5 stars
    Focused on AutoML
    Reviewed in the United States on June 16, 2021
    Format: Paperback
    What I appreciated most about this book was the focus on Automated Machine Learning. Artificial Intelligence is a huge subject, and Adnan does an excellent job of introducing AutoML and not delving into areas not primarily concerned with AutoML. The book covers...
    What I appreciated most about this book was the focus on Automated Machine Learning. Artificial Intelligence is a huge subject, and Adnan does an excellent job of introducing AutoML and not delving into areas not primarily concerned with AutoML.

    The book covers the background of AutoML and its main aspects including algorithms and techniques. The main open source and commercial platforms and tools are described while only a few of the open source platforms have examples. I was happy with this as I was most interested in understanding the cloud platforms support for AutoML.

    The book is broken into three main sections: Introduction to AutoML, AutoML with Cloud Platforms, and Applied Automated Machine Learning. The third section was a nice bonus as it provides insight into how AutoML fits within an enterprise and advice around it adoption.

    If you are interested in or new to AutoML or are in the industry and are looking for a broader perspective, this would be a great book to pick up. If you are looking for a deep dive into a specific AutoML platform, then you might be disappointed. In this regards, hopefully we can encourage Adnan to write more books on AutoML.
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