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

Learning how to build a human review workflow

Before you get started with creating a human review workflow with Amazon A2I, you will need an S3 bucket in the same AWS Region to collect output data for the workflow.

Creating a labeling workforce

To build a human review workflow, you will need to define the labeling workforce who will label your dataset. As we learned in Chapter 9, you can choose either a public (Mechanical Turk), private (in-house), or vendor (AWS Marketplace/third-party) workforce to review your dataset. You can create and manage workforces from Amazon Ground Truth’s console. If you have an existing workforce in Ground Truth, you can use the same workforce to review predictions.

Setting up an A2I human review workflow or flow definition

As we learned in the Core concepts of Amazon A2I section, next, we’ll create a human review workflow to define the task types, the activation conditions if you’re using a built-in task type, task template...