This is the code repository for Hands-On Artificial Intelligence on Amazon Web Services , published by Packt.
Decrease the time to market for AI and ML applications with the power of AWS
From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS.
This book covers the following exciting features:
- Gain useful insights into different machine and deep learning models
- Build and deploy robust deep learning systems to production
- Train machine and deep learning models with diverse infrastructure specifications
- Scale AI apps without dealing with the complexity of managing the underlying infrastructure
- Monitor and Manage AI experiments efficiently
- Create AI apps using AWS pre-trained AI services
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
{
"Image": {
"Bytes”: “...”
}
}
Following is what you need for this book: This book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. A basic understanding of machine learning concepts is expected.
With the following software and hardware list you can run all code files present in the book (Chapter 1-13).
| Chapter | Software required | OS required |
|---|---|---|
| All | AWS | Windows, Mac OS X, and Linux (Any) |
| All | Python 3.6+ | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Subhashini Tripuraneni has several years of experience leading AI initiatives in financial services and convenience retail. She has automated multiple business processes and helped to create a proactive competitive advantage for businesses via AI. She is also a seasoned data scientist, with hands-on experience building machine learning and deep learning models in a public cloud. She holds an MBA from Wharton Business School, with a specialization in business analytics, marketing and operations, and entrepreneurial management. In her spare time, she enjoys going to theme parks and spending time with her children. She currently lives in Dallas, TX, with her husband and children.
Charles Song is a solutions architect with a background in applied software engineering research. He is skilled in software development, architecture design, and machine learning, with a proven ability to utilize emerging technologies to devise innovative solutions. He has applied machine learning to many research and industry projects, and published peer-reviewed papers on the subject. He holds a PhD in computer science from the University of Maryland. He has taught several software engineering courses at the University of Maryland for close to a decade. In his spare time, he likes to relax in front of his planted aquariums, but also enjoys martial arts, cycling, and snowboarding. He currently resides in Bethesda, MD, with his wife.
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