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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

Deploying a model at the edge using Lookout for Vision and AWS IoT Greengrass

In this section, we will learn how to deploy a model that was trained using Lookout for Vision to an edge device using AWS IoT Greengrass V2. For this hands-on example, we will create an EC2 instance to simulate an edge device.

Step 1 – Launch an Amazon EC2 instance

The first step is to launch an EC2 instance where we will install AWS IoT Greengrass V2. We will use an Ubuntu 20.04 c5.2xlarge instance. Now, navigate to EC2 via the AWS Management Console (https://us-east-1.console.aws.amazon.com/ec2/home?region=us-east-1#Home). Select on Launch instance:

Figure 8.2: Launching an instance using the AWS Management Console

Figure 8.2: Launching an instance using the AWS Management Console

Enter a Name value and under the Quick Start section, select the Ubuntu Server 20.04 Amazon Machine Image. Confirm that the architecture is x86. Under Instance type, select c5.2xlarge. Keep the default values for the rest of the configuration details...

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