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

Defining a problem that CV can solve and processing data

The critical first step before designing a CV system is to define the problem you’re trying to solve and the desired business outcomes. Involve the relevant stakeholders to identify any pain points and a solution that could be solved with CV. Understand the constraints and requirements to solve the problem and evaluate the available data. Specify what success looks like and identify the key performance indicators (KPIs). Consider the costs, resources available, and security and compliance requirements. Here is a list of additional questions to ask during this process:

  • What are the goals of the solution? Is it to increase revenue, provide actionable insights, reduce manual processes, or something else?
  • What is the timeline for the solution?
  • How will the model be integrated into downstream systems?
  • What type of data will be processed?
  • What are the data privacy requirements and how will these be addressed...