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 Amazon SageMaker for governance

Throughout this chapter, we have detailed the importance of establishing an AI governance framework. However, setting up an overall process to gain visibility of performance, control access, audit changes, and mitigate bias is no easy feat. To help address these challenges and remove undifferentiated heavy lifting, SageMaker provides purpose-built tools to help implement governance.

ML governance capabilities with Amazon SageMaker

As user adoption increases, it becomes more difficult for administrators to manage user access to ML projects. Custom permissions policies are often required for different ML user groups and these permissions sets vary greatly. Customization is a time-consuming process that could delay user onboarding. Amazon SageMaker Role Manager (https://docs.aws.amazon.com/sagemaker/latest/dg/role-manager.html) simplifies this process by providing a baseline set of permissions for different user personas and ML activities through...