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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

Summary

The user-generated content era represents opportunities for users to collaborate and socialize in rich media formats. The growth of user communities in both size and diversity drives engagement around your products and services. For web and mobile platforms to monetize that traffic, they require capabilities to build safe and inclusive environments. These communities will become toxic and have inappropriate content without adequate protection and governance structures.

Amazon Rekognition can moderate images without needing a data science team. You learned how to use the DetectContentModeration API to quickly and easily discover top-level and secondary inappropriate categories. Then, you built automation to flag content on granular and coarse levels depending on your business requirements.

These capabilities are critical to modern platforms but didn’t go far enough. End users have expectations of content beyond images, in the form of video clips. You learned how...

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