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Computer Vision on AWS

Computer Vision on AWS

By : Lauren Mullennex, Nate Bachmeier, Jay Rao
4.9 (8)
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Computer Vision on AWS

Computer Vision on AWS

4.9 (8)
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)
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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 Amazon A2I

Building a human review system is quite complex, time-consuming, and expensive. Typically, it requires you to build custom applications to implement workflows and manage review tasks and consolidate results. Additionally, it needs to handle the human reviewer, who will work on the assigned tasks and submit results. Amazon A2I streamlines this process. It provides built-in human review workflows for common use cases, or you can build your own workflow.

With Amazon A2I, you can build the following workflow. Your input data will be sent to an AWS AI service (such as Textract or Rekognition) or custom ML models (hosted with SageMaker or self-managed endpoints). You can set up rules to send high-confidence predictions to client applications immediately and send low-confidence results to human reviewers via a human review workflow setup with Amazon A2I. Once the human reviewers review the predictions, their responses will be consolidated and stored on S3. The client...

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