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

Applying AI governance in CV

One example CV use case is automating a facial identification workflow. Manually verifying a person’s identity is a process that is time-consuming, inaccurate, and difficult to scale. Using biometrics and CV algorithms for identity verification can reduce friction during the customer onboarding process and lead to a better customer experience. Another application of CV is in fraud detection. Identifying anomalies in data helps catch and prevent fraudulent transactions. However, there are additional risks involved in automating facial recognition and fraud prevention workflows. Biased datasets could lead to models making predictions based on gender, income, ethnicity, age, and other discriminatory characteristics. To help mitigate these added risks and promote algorithmic fairness, it is important to have an ethical AI governance model that continuously reinforces transparency and the decision-making processes of AI systems.

Types of biases