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)
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

Detecting Labels using the API

Developers interact with Amazon Rekognition using the AWS Command-Line Interface (AWS CLI), the AWS SDK, and REST clients. This section will use the boto3 module for Python.

Uploading the images to S3

Use the AWS CLI or the Amazon S3 console to create a bucket for your test images. This example command will provision the cv-on-aws-book-xxxx bucket in Ohio Region (us-east-2). Bucket names must be globally unique, so specify any random suffix. Next, record this value as you’ll need it later:

$ aws s3 mb --region us-east-2 s3://cv-on-aws-book-nbachmei
make_bucket: cv-on-aws-book-nbachmei

Next, upload the sample files from the book’s GitHub repository. You can complete this step using the following command:

$ aws s3 sync 02_IntroRekognition/images s3://cv-on-aws-book-nbachmei/chapter_02/images --region us-east-2

Initializing the boto3 client

Open your Jupyter Notebook using the steps from Chapter 1. Import DetectLabel.ipynb...