This is the code repository for Computer Vision on AWS, published by Packt.
Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker
Computer vision (CV) is a field of artificial intelligence that helps transform visual data into actionable insights to solve a wide range of business challenges. This book provides prescriptive guidance to anyone looking to learn how to approach CV problems for quickly building and deploying production-ready models. You’ll begin by exploring the applications of CV and the features of Amazon Rekognition and Amazon Lookout for Vision. The book will then walk you through real-world use cases such as identity verification, real-time video analysis, content moderation, and detecting manufacturing defects that’ll enable you to understand how to implement AWS AI/ML services. As you make progress, you'll also use Amazon SageMaker for data annotation, training, and deploying CV models. In the concluding chapters, you'll work with practical code examples, and discover best practices and design principles for scaling, reducing cost, improving the security posture, and mitigating bias of CV workloads. By the end of this AWS book, you'll be able to accelerate your business outcomes by building and implementing CV into your production environments with the help of AWS AI/ML services.
This book covers the following exciting features:
- Apply CV across industries, including e-commerce, logistics, and media
- Build custom image classifiers with Amazon Rekognition Custom Labels
- Create automated end-to-end CV workflows on AWS
- Detect product defects on edge devices using Amazon Lookout for Vision
- Build, deploy, and monitor CV models using Amazon SageMaker
- Discover best practices for designing and evaluating CV workloads
- Develop an AI governance strategy across the entire machine learning life cycle
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
{
"SubscriptionArn": "arn:aws:sns:region:account:AmazonRekognitionPersonTrackingTopic:04877b15-7c19-4ce5-b958-969c5b9a1ecb"
}
Following is what you need for this book: If you are a machine learning engineer or data scientist looking to discover best practices and learn how to build comprehensive CV solutions on AWS, this book is for you. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.
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 |
|---|---|---|
| 1-13 | Access to or signing up for an AWS account | Windows, Mac OS X, and Linux (Any) |
| 1-13 | Jupyter Notebook | Windows, Mac OS X, and Linux (Any) |
Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS. She has broad experience in infrastructure, DevOps, and cloud architecture across multiple industries. She has published multiple AWS AI/ML blogs, spoken at AWS conferences, and focuses on developing solutions using CV and MLOps.
Nate Bachmeier is a Principal Solutions Architect at AWS (Ph.D. CS, MBA). He nomadically explores the world one cloud integration at a time, focusing on the Financial Service industry.
Jay Rao is a Principal Solutions Architect at AWS. He enjoys providing technical and strategic guidance to customers and helping them design and implement solutions.