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

Applying AI Governance in CV

In the previous chapter, we covered best practices for designing an end-to-end CV pipeline.
We discussed how you can use these guidelines throughout the ML life cycle to build, deploy, and manage reliable and scalable CV workflows.

In this chapter, we will discuss AI governance and its importance in CV. You may be asking, “How is AI governance relevant to my role as an AI/ML practitioner?” Security and compliance are only a small facet of the components of an AI governance strategy. The lack of existence of an organizational AI governance strategy has implications across the entire ML life cycle. From data collection to deploying and monitoring models, as an AI/ML practitioner it’s your responsibility to work with other business stakeholders to ensure ML models are performing as expected, and to address problems that arise quickly and efficiently.

The increasing speed and scale of model development and deployment has created...