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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.
This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.
By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.
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Seitenzahl: 671
Veröffentlichungsjahr: 2021
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Machine Learning with Amazon SageMaker Cookbook
80 proven recipes for data scientists and developers to perform machine learning experiments and deployments
Joshua Arvin Lat
BIRMINGHAM—MUMBAI
Machine Learning with Amazon SageMaker Cookbook
Copyright © 2021 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Publishing Product Manager: Sunith Shetty
Senior Editor: Mohammed Yusuf Imaratwale
Content Development Editor: Nazia Shaikh
Technical Editor: Arjun Varma
Copy Editor: Safis Editing
Project Coordinator: Aparna Ravikumar Nair
Proofreader: Safis Editing
Indexer: Tejal Daruwale Soni
Production Designer: Aparna Bhagat
First published: October 2021
Production reference: 2280921
Published by Packt Publishing Ltd.
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ISBN 978-1-80056-703-0
www.packt.com
Dear reader. Thank you for purchasing this book! Years ago, I relied on "cookbooks" to help me gain the hands-on skills needed to get the job done using tech frameworks, libraries, tools, and services. It is my turn to give back to the tech community and provide you a "cookbook" with practical and complete solutions to help you in your machine learning journey. I hope this book helps you achieve your goals and dreams as well.
First, I would like to acknowledge Sunith Shetty, Gebin George, Aparna Nair, Nazia Shaikh, Arjun Varma, Shifa Ansari, and everyone from the Packt team for making this book a success. I would also like to thank Raphael Jambalos, Mark Jimenez, and Lauren Yu for their patient support in helping significantly improve the quality of this book. Writing a book is a team game and I am grateful to everyone who has contributed to this book.
Next, I would also like to thank Ross Barich, Julien Simon, Cameron Peron, and everyone from the AWS team for the advice and support that helped me write this book. I would also like to give my sincere thanks to the AWS teams who have built, developed, and managed the different features and capabilities of Amazon SageMaker. I would also like to acknowledge and thank Raphael Quisumbing and the leaders of AWS User Group Philippines. Years ago, it was just me, Raphael Quisumbing, Diwa del Mundo, and Mike Rayco, leading and organizing these tech events. Now, the user group has grown significantly bigger and we now have more leaders and contributors trying to make the tech world a better place.
I would like to give my sincere thanks to my parents and my sister for their never-ending love and support. At the same time, I would like to thank my relatives, friends, and colleagues at work. I would not be able to list all your names here but this acknowledgment section would not be complete without giving credit to the support and advice you all have given me throughout the years.
Finally, I want to dedicate this book to Sophie Soliven, who has been very supportive in my career choices and decisions. It all started with the "commute adventure" years ago and we did not expect that to become a lifelong journey.
Contributors
About the author
Joshua Arvin Lat is the Chief Technology Officer (CTO) of NuWorks Interactive Labs, Inc. He previously served as the CTO of three Australian-owned companies and also served as the Director for Software Development and Engineering for multiple e-commerce start-ups in the past, which allowed him to be more effective as a leader. Years ago, he and his team won first place in a global cybersecurity competition with their published research paper. He is also an AWS Machine Learning Hero and has shared his knowledge at several international conferences, discussing practical strategies on machine learning, engineering, security, and management.
About the reviewers
Lauren Yu is a former software engineer currently pursuing a career in law. She previously worked at AWS on Amazon SageMaker, primarily focusing on the SageMaker Python SDK, as well as toolkits and Docker images for integrating deep learning frameworks into Amazon SageMaker. While at Amazon, she also helped co-found the Amazon Symphony Orchestra of Seattle. In her spare time, she enjoys playing viola and learning more about the intersection of law and technology.
Raphael Jambalos is a cloud-native developer with 8 years of experience developing in Ruby and Python. He currently leads the cloud-native development team of eCloudValley Philippines, focused on designing and implementing various solutions such as serverless applications, CI/CD pipelines, load testing, and web development. He also holds four AWS certifications, with all three Associate-level certs and a Specialty certification in security.
Mark Jimenez is a software developer with a decade of experience in the industry ranging from web development and mobile development to machine learning. He holds several AWS certifications, including the AWS Certified Machine Learning – Specialty, AWS Certified Developer – Associate, and AWS Certified Solutions Architect – Associate certifications.
