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

Part 5: Best Practices for Production-Ready CV Workloads

This fifth part consists of three cumulative chapters that will cover how to improve the accuracy of CV workloads using human reviewers, best practices to consider for your end-to-end CV pipelines, and the importance of establishing AI governance.

By the end of this part, you will understand how to use Amazon Augmented AI (Amazon A2I) to improve the accuracy of CV workflows, best practices for implementing cost optimization and security, and steps for applying AI governance.

This part comprises the following chapters:

  • Chapter 11, Integrating Human-in-the-Loop with Amazon Augmented AI
  • Chapter 12, Best Practices for Designing an End-to-End CV Pipeline
  • Chapter 13, Applying AI Governance in CV