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

Labeling Packt logos in images using Amazon SageMaker Ground Truth

In this section, we will learn how to create a labeling job to label Packt logo images. We will use a built-in task type (bounding box), will use Amazon S3 to place the input dataset, and will select a private labeling workforce.

Step 1 – collect your images

Upload the sample images from the book’s GitHub repository. You can complete this step using the following command:

$ aws s3 sync 09_SageMaker_Ground_Truth/images s3://cv-on-aws-book-xxxx/chapter_09/images --region us-east-2

Important note

To collect the sample images, you can use the same S3 bucket you created in Chapter 2.

Now, navigate to Amazon Lookout for Vision on the AWS Management Console (

Select on Ground Truth.

Figure 9.1: Amazon SageMaker Console

Figure 9.1: Amazon SageMaker Console

Important note

Your work team, input manifest file, output...