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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Once the model is hosted, you can start analyzing your images using the DetectAnomalies API.”

A block of code is set as follows:

    "SubscriptionArn": "arn:aws:sns:region:account:AmazonRekognitionPersonTrackingTopic:04877b15-7c19-4ce5-b958-969c5b9a1ecb"

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

[aws sns subscribe \
  --region us-east-2 \
  --topic-arn arn:aws:sns:region:account:AmazonRekognitionPersonTrackingTopic \
  --protocol sqs \
  --notification-endpoint arn:aws:sqs:region:account:PersonTrackingQueue

Any command-line input or output is written as follows:

$ git clone
$ cd Computer-Vision-on-AWS/07_LookoutForVision

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “If you’re a first-time user of the service, it will ask permission to create an S3 bucket to store your project files. Click Create S3 bucket.”

Tips or important notes

Appear like this.