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


Amazon SageMaker is a comprehensive suite of services and capabilities that aims to bring ML to every developer, business analyst, and data scientist. You can mix and match its tooling with your existing environment or leverage it entirely for your ML model needs.

You also recreated Jian Yang’s famous hotdog classifier using the AWS console and the built-in image classifier. While this step was more involved than Chapter 4, it didn’t require a Ph.D. in data science. Hopefully, that gives you the confidence to experiment with twenty-four built-in algorithms plus the thousands available through AWS Marketplace.

You also observed how Amazon SageMaker’s CloudWatch integration standardizes where you look for performance and troubleshooting information. Within a matter of clicks, you can drill down to specific error messages. Finally, the AWS CLI simplifies exporting the job definitions for programmatic access. Then, you learned that even datasets that...