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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

Creating Custom Models with Amazon Rekognition Custom Labels

In the last chapter, we learned about Amazon Rekognition’s capability to identify objects in images and videos using built-in APIs. However, there are situations where you’re looking for certain things that have specific meaning to you or your business. For example, imagine you’re an auto parts manufacturer and want to identify different parts such as a crankshaft, torque converter, or radiator. Typically, generic machine learning (ML) models would identify these parts as auto parts, but that label may not be specific enough for your needs. Take another scenario—if you’re a car enthusiast, you’d know the logos of different car manufacturers. If you would like to detect the logos of a car manufacturer using computer vision (CV), you’d need to build a customized ML model that can differentiate the logos of different car manufacturers. This is where Rekognition Custom Labels will...

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