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)
1
Part 1: Introduction to CV on AWS and Amazon Rekognition
5
Part 2: Applying CV to Real-World Use Cases
9
Part 3: CV at the edge
12
Part 4: Building CV Solutions with Amazon SageMaker
15
Part 5: Best Practices for Production-Ready CV Workloads

Deploying and monitoring a CV model

Once a CV model has been deployed, you should regularly evaluate its performance to establish when retraining is required and perform deployment testing before rolling out a new version. This ensures that models are delivering reliable results and that they are meeting your established business outcomes. Deployment testing helps you detect changes in a model’s accuracy and identify any errors in a model’s implementation before it is deployed to production.

Shadow testing

Shadow testing is a testing technique for evaluating the performance of a model before it’s rolled out to production. A new (shadow) ML model is tested in a production environment without impacting actual user traffic. The shadow model’s predictions are not used in the production application; instead, the shadow model runs alongside the existing production model. A copy of the inference requests is routed to the shadow model and its predictions are...