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


As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.


A2I human review workflow

setting up 244

A/B testing 266

AI governance 276

applying, in CV 280

audits, performing 277, 278

bias, detecting 277

business stakeholders, responsibilities 279

compliance, defining 277

data risks 277

documentation, defining 277

MLOps 278

monitoring and visibility 278

risks, defining 277

traceability 277, 278

versioning 277, 278

AI governance, in CV

biases, types 280

bias, mitigating in identity verification workflows 281

AI/ML architects 279

algorithmic bias 281

Amazon A2I 242, 243

core concepts 243

human-in-the loop 243

human loops 243

human review workflow or flow definition 243

task types 243

Amazon A2I, with Amazon Rekognition

human loop, starting 252-256

human loop status...