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Noah Gift - Pragmatic AI: An Introduction to Cloud-Based Machine Learning

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Noah Gift Pragmatic AI: An Introduction to Cloud-Based Machine Learning
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Master Powerful Off-the-Shelf Business Solutions for AI and Machine LearningPragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get resultseven if you dont have a strong background in math or data science.Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, youll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, youll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether youre a business professional, decision-maker, student, or programmer, Gifts expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools youll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish

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Praise for Pragmatic AI

[This is] a sweeping guide that bridges the gap between the promise of AI and solutions to the gritty problems that must be solved to deploy real-world projects. Clear and usable, Pragmatic AI covers much more than just Python and AI algorithms.

Christopher Brousseau, founder and CEO of Surface Owl, the Enterprise AI platform

A fantastic addition for any technology enthusiast! There is so much you can say about this book! Noah Gift really made this a practical guide for anyone involved with machine learning in the industry. Not only does it explain how one can apply machine learning on large data sets, it provides a valuable perspective on technology feedback loops. This book will benefit many data science and development teams so they can create and maintain their applications efficiently right from the beginning.

Nivas Durairaj, technical account manager, AWS (Certified Professional Architect AWS)

A great read if you want insights into building production-quality ML pipelines and tools that truly help your data engineering, data science, or data DevOps team. Even experienced developers often find themselves spinning their wheels on low-productivity tasks. Oftentimes, software books and university classes don't explain the steps needed to go into production. Noah has a gift for finding pragmatic approaches to software deployments that can really accelerate the development and delivery process. He has a focus and passion for enabling rapid software solutions that is very unique.

The key to building production-quality ML pipelines is automation. Tasks and steps, which engineers may do manually during the research or prototype phase, must be automated and scaled in order to create a production system. This book is full of practical and fun exercises that will help any Python developer automate and extend their pipelines into the cloud.

I'm currently working with big data, ML pipelines, Python, AWS, Google cloud, and Azure at Roofstock.com, an online real estate company. Our analytics database is approaching 500 million rows! Within this book I found many practical tips and exercises that will immediately improve my own productivity. Recommended!

Michael Vierling, lead engineer, Roofstock

Pragmatic AI

An Introduction to Cloud-Based Machine Learning

Noah Gift

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Library of Congress Control Number: 2018939834

Copyright 2019 Pearson Education, Inc.

All rights reserved. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information regarding permissions, request forms and the appropriate contacts within the Pearson Education Global Rights & Permissions Department, visit www.pearsoned.com/permissions/.

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Microsoft Windows and Microsoft Azure are registered trademarks of the Microsoft Corporation in the U.S.A. and other countries. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation.

Google, GCP and the Google logo are registered trademarks of Google Inc., used with permission.

Amazon, AWS and the Amazon logo are registered trademarks of Amazon Inc., used with permission.

ISBN-13: 978-0-13-486386-3
ISBN-10: 0-13-486386-0

This book is dedicated to my family
and extended family, who have always been there for me: my wife, Leah Gift;
my mom, Shari Gift; my son, Liam Gift; and my mentor,
Dr. Joseph Bogen

Contents
Preface

About twenty years ago I was working at Caltech in Pasadena and I dreamed of someday working with AI on a daily basis. At the time, in the early 2000s, it was not a popular time to be interested in AI. Despite that, here we are, and this book culminates a lifelong obsession with AI and science fiction. I feel very lucky to have rubbed elbows with some of top people in AI while at Caltech, and undoubtedly this experience led me down the road to writing this book.

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