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

Best Practices for Designing an End-to-End CV Pipeline

In the previous chapter, we introduced Amazon Augmented AI (A2I). We discussed the importance of a human-in-the-loop workflow and walked through a code example while using Amazon Rekognition to analyze unsafe images.

Throughout this book, we’ve covered several real-world CV use cases. We’ve also discussed AWS AI/ML services in detail, including Rekognition, Lookout for Vision, and SageMaker. To recap, AWS AI/ML services are managed services, which means that the undifferentiated heavy lifting of patching, upgrading, and maintaining servers and hardware is removed. AWS AI services are composed of serverless architecture that scales automatically. Rekognition includes pre-trained capabilities, which means that steps such as preparing and transforming data, selecting an algorithm to train the model, and tuning the model are not required. SageMaker has some distinguishing characteristics compared to AWS AI services...