Book Image

Computer Vision on AWS

By : Lauren Mullennex, Nate Bachmeier, Jay Rao
Book Image

Computer Vision on AWS

By: Lauren Mullennex, Nate Bachmeier, Jay Rao

Overview of this book

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.
Table of Contents (21 chapters)
1
Part 1: Introduction to CV on AWS and Amazon Rekognition
5
Part 2: Applying CV to Real-World Use Cases
9
Part 3: CV at the edge
12
Part 4: Building CV Solutions with Amazon SageMaker
15
Part 5: Best Practices for Production-Ready CV Workloads

Summary

Countless business processes rely on identifying and classifying humans. In this chapter, you learned how Amazon Rekognition makes it easy to automate those processes using facial metadata.

For example, you can quickly add capabilities to predict age, gender, and emotional state to your applications. These capabilities enable scenarios such as detecting underage customers on a casino gaming floor. You can also preemptively notice which customers deserve more attention or are becoming hostile.

You learned about bringing these various capabilities together by building a contactless identity verification system. That solution includes building blocks for registering, authenticating, and updating customers’ profiles. Then you added support for corporate and government identification cards using Amazon Textract.

In the next chapter, you’ll learn how to automate a video analysis pipeline using IP cameras and OpenCV. That solution demonstrates some of the many...