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)
Part 1: Introduction to CV on AWS and Amazon Rekognition
Part 2: Applying CV to Real-World Use Cases
Part 3: CV at the edge
Part 4: Building CV Solutions with Amazon SageMaker
Part 5: Best Practices for Production-Ready CV Workloads

Developing a CV model

Once you have a training dataset, you move to the model development stage, which includes training, evaluating, and tuning a model. Algorithm selection is not a one-size-fits-all process and model development is iterative. In this section, we will not dive deep into training algorithms, but will instead highlight a few best practices and the training options available with AWS AI/ML services.


In Chapter 1, we briefly covered a few types of algorithms that are often used to solve CV use cases, such as object detection, classification, and segmentation. There are many algorithms available to solve CV problems and choosing the best one depends on a few different factors, including the type of problem you are trying to solve, the data you have available, and your specific performance requirements. Deep learning algorithms often work well for CV, but other more traditional ML algorithms such as Support Vector Machines (SVMs), random forests, and k-nearest...