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)
Part 1: Introduction to CV on AWS and Amazon Rekognition
Part 2: Applying CV to Real-World Use Cases
Part 3: CV at the edge
Part 4: Building CV Solutions with Amazon SageMaker
Part 5: Best Practices for Production-Ready CV Workloads


When humans view visual information, we instinctively detect labels for objects, scenes, and activities. Amazon Rekognition offers similar capabilities as easy-to-use APIs that don’t require machine learning expertise. Using the Rekognition management console, you learned how to upload messages and view the response payloads. Next, you programmatically repeated that process using the Python boto3 module. Additionally, this chapter introduced the PIL for drawing bounding boxes on the example images.

Amazon Rekognition’s built-in label detection supports over 2,500 labels with more than 250 pieces of supporting bounding box information. While this breadth covers numerous use cases, it won’t cover every business-specific need, such as finding Packt Publishing’s logo. In that case, you’ll need to train Amazon Rekognition Custom Labels. Join us in the next chapter to learn how to use this feature without requiring undifferentiated heavy lifting...