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

Introducing Amazon Lookout for Vision

As we discussed in Chapter 3, building a custom ML model from scratch is an overwhelming undertaking that requires tremendous time, not to mention that it requires thousands of images along with expert ML practitioners.

With Amazon Lookout for Vision, you don’t need to invest your time and resources in building ML or deep learning pipelines. Instead, you just need to provide normal and anomalous images and the service does the rest. If your images are already labeled, you can directly import them; otherwise, you can use the service’s built-in labeling interface or utilize SageMaker GroundTruth for labeling. Once you have labeled data, you just need to initiate single-click training. Once the model training completes, you will receive results showing the model’s performance.

You can get started with Lookout for Vision with few images (as few as 30 images) but you may want to provide a larger dataset for complex use cases...