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

Introducing collections

Amazon Rekognition stores facial information inside server-side containers known as collections. Collections represent a logical grouping of Face Metadata, not the original image of the person.
It supports operations for indexing, listing, searching, and deleting faces.

There’s no charge for collections; each holds 10 million Face Metadata, and you only pay for the aggregate Faces Metadata Storage ($0.00001/per facial metadata per month). For example, storing one million faces costs $1 per month, regardless of the total spanning collections.

Creating a collection

You can create a collection using the AWS Command Line Interface (AWS CLI) v2 or the AWS SDK. When you invoke the CreateCollection API, it only requires a name and returns instantly:

$ aws rekognition create-collection --collection-id \ "HelloWorld"

This command will report the following output:

{
  «StatusCode": 200,
  «CollectionArn...