Julien Simon - Learn Amazon SageMaker: A guide to building, training, and deploying machine learning models for developers and data scientists

A guide to building, training, and deploying machine learning models for developers and data scientists

Julien Simon

BIRMINGHAMMUMBAI

Copyright 2020 Packt Publishing

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Senior Editor: David Sugarman

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First published: August 2020

Production reference: 1260820

Published by Packt Publishing Ltd.

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ISBN 978-1-80020-891-9

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Julien Simon is a principal AI and machine learning developer advocate. He focuses on helping developers and enterprises to bring their ideas to life. He frequently speaks at conferences and blogs on AWS blogs and on Medium. Prior to joining AWS, Julien served for 10 years as CTO/VP of engineering in top-tier web start-ups where he led large software and ops teams in charge of thousands of servers worldwide. In the process, he fought his way through a wide range of technical, business, and procurement issues, which helped him gain a deep understanding of physical infrastructure, its limitations, and how cloud computing can help.

Chaitanya Hazarey is a technical leader and machine learning architect with the AWS Machine Learning Product Management team. He helps customers design and build cloud native machine learning products. Driven to solve hard problems, he engages with partners and customers to modernize their machine learning stack and integrate with Amazon SageMaker, working together with the business and engineering teams to make products successful. He has a master's degree in computer science from USC, LA. He enjoys teaching at Amazon's Machine Learning University and his research interests include efficient training and inference techniques for deep learning algorithms.

Javier Ramirez is a Developer Advocate at AWS. He was previously head of engineering at Datatonic, an ML consultancy; cofounder and data engineer at Teowakia big data consultancy; CTO at ASPGemsa web development agency; and a Google Developer Expert in GCP for over 5 years. He has also cofounded Aprendoaprogramar and Utende in a career spanning over 20 years. He loves data, big and small, and he has extensive experience with SQL, NoSQL, graphs, in-memory databases, big data, and machine learning. He likes distributed, scalable, always-on systems. He has presented at events in over 20 countries, mentored dozens of start-ups, taught for 6 years at different universities, and trained hundreds of professionals on cloud, data engineering, and ML.

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Dear reader, please accept my most sincere thanks for spending your hard-earned money on this book. I hope it will live up to your expectations, and help you grow as a Machine Learning professional.

Writing it is the conclusion of a process that started 40 years ago, when I opened a BASIC programming book for the Commodore VIC-20. Over the years, I've bought and read more technical books than I can remember, and writing one is a dream come true. I can only hope that traces of the undying brilliance found in the works of Donald Knuth, Brian Kernighan, Dennis Ritchie, Kirk McKusick, Richard Stevens, and Evi Nemeth somehow rubbed off on me during the years I spent reading them, but I very seriously doubt it.

First, I'd like to address my most sincere thanks to the Packt team, especially my editors Joseph Sunil and David Sugarman for their valuable feedback and advice. I never thought writing a book would be this painless. I would also like to thank my reviewers, particularly Javier Ramirez and Ricardo Sueiras, for helping me make this book better than it originally was.

I'd also like to thank the AWS service teams who work on Amazon SageMaker every day, and sometimes every night, to give our customers the best possible features and service quality. You are the unspoken heroes of this book, and I'm but your messenger. I hope I did you justice.

On a personal note, I'm forever indebted to my parents for their never-ending support, and for never pulling the literal plug on me when other kids were playing outside, or when the moon was already high the sky. Back then, I did take the road less traveled, and these long days and nights spent dissecting Commodores, Apple ][s and Macs made all the difference.

My love also goes to my wife and children, who somehow kept the house calm enough during COVID-19 lockdown for me to write 13 chapters in 3 months, at my own amazement. Now, I promise I'll take more time to enjoy these sunny afternoons.