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

Moderating Content with AWS AI Services

Modern web and mobile applications need capabilities for users to collaborate and socialize. These features are becoming table stakes, with over 80% of the internet becoming user-generated content. Think about the sheer volume of online interactions, product reviews, and brand interactions you’ve made this week alone. It’s no wonder they call it the user-generated content era.

In parallel, mobile devices are evolving how customers expect to engage with social features. They demand multi-modal capabilities that span audio, video, and rich-text documents. While this drives platform adoption, it also attracts trolls that bring toxicity and extremism.

Traditional businesses ask their customers and human moderators to flag inappropriate content. However, relying on customers to report negative experiences isn’t a good strategy and will hurt engagement. Meanwhile, maintaining a human workforce is too expensive to scale. Building...