Abhishek Mishra - Machine Learning in the AWS Cloud : Add Intelligence to Applications with Amazon SageMaker and Amazon Rekognition

1. Chapter 1
2. Chapter 7
3. Chapter 9
4. Chapter 12
5. Chapter 14
6. Chapter 15
7. Chapter 18
8. Appendix C
9. Appendix D

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. Chapter 4
5. Chapter 5
6. Chapter 6
7. Chapter 7
8. Chapter 8
9. Chapter 9
10. Chapter 10
11. Chapter 11
12. Chapter 12
13. Chapter 13
14. Chapter 14
15. Chapter 15
16. Chapter 16
17. Chapter 17
18. Chapter 18

Abhishek Mishra

Copyright 2019 by John Wiley & Sons, Inc., Indianapolis, Indiana
Published simultaneously in Canada

ISBN: 978-1-119-55671-8
ISBN: 978-1-119-55673-2 (ebk.)
ISBN: 978-1-119-55672-5 (ebk.)

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1. To my wife Sonam, for her love and support through all the years we've been together.

   To my daughter Elana, for bringing joy and happiness into our lives.

   Abhishek

This book would not have been possible without the support of the team at Wiley, including Jim Minatel, Kenyon Brown, David Clark, Kim Cofer, and Pete Gaughan. I would also like to thank Chaim Krause for his keen eye for detail. It has been my privilege to work with all of you. Thank you.

Abhishek Mishra has been active in the IT industry for over 19 years and has extensive experience with a wide range of programming languages, enterprise systems, service architectures, and platforms.

He holds a master's degree in computer science from the University of London and currently provides consultancy services to Lloyds Banking Group in London as a security and fraud solution architect. He is the author of several books, including Amazon Web Services for Mobile Developers.

Chaim Krause is a lover of computers, electronics, animals, and electronic music. He's tickled pink when he can combine two or more in some project. He has come by the vast majority of his knowledge through independent learning. He jokes with everyone that the only difference between what he does at home and what he does at work is the logon he uses. As a lifelong learner he is often frustrated with technical errors in documentation that waste valuable time and cause unnecessary frustration. One of the reasons he works as the technical editor on books is to help others avoid those same pitfalls.

Amazon Web Services (AWS) is one of the leading cloud-computing platforms in the industry today. At the time this book was written, AWS offered more than 100 services, each of which resided in one of 18 different service categories. For someone who is new to cloud computing or to the AWS ecosystem, the sheer number of services on offer can be daunting. It can be difficult to know where to begin and what services to focus on.

Developers who are new to machine learning as well as experienced data scientists are often not aware of the power of the public cloud and AWS's offerings in the machine learning space in particular. In the past, cloud-based machine learning offerings have been limited in the types of algorithms they could support and the level of customization that was possible. All of this changed when Amazon announced SageMakera service that provided the ability to build machine learning models based on Amazon's implementation of cutting-edge algorithms, as well as the option to build custom models with frameworks such as Scikit-learn and Google TensorFlow.

Real-world use cases of cloud-based machine learning models are not based on using the model in isolation, but instead rely on a number of supporting systems such as databases, load balancers, API gateways, and identity providers, all of which are provided by AWS. This book is written to provide both seasoned machine learning experts and enthusiasts alike an introduction to a selection of AWS machine learning services that are based on pre-trained models, as well as step-by-step examples of how to train and deploy your own custom models on Amazon SageMaker. For enthusiasts who are new to machine learning, this book also provides a selection of chapters that cover the fundamentals of machine learning such as data preprocessing, visualization, feature engineering, and the use of common Python libraries such as NumPy, Pandas, and Scikit-learn.