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

Using the AWS Well-Architected Framework

The AWS Well-Architected Framework (https://docs.aws.amazon.com/wellarchitected/latest/framework/welcome.html) helps you design and evaluate your infrastructure to ensure it is secure, efficient, cost-optimized, reliable, and sustainable on AWS. The framework provides guidance and considerations for building and operating your workloads on AWS. It includes questions that assist you with identifying areas for improvement that focus on six pillars: cost optimization, operational excellence, reliability, performance efficiency, security, and sustainability. In addition, the AWS Well-Architected Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/machine-learning-lens.html) is a resource for evaluating ML-specific workloads. Let’s address the best practices to consider for each pillar when architecting CV workloads.

Cost optimization

Cost optimization includes understanding how to manage your...