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

Introducing Amazon SageMaker Ground Truth

Amazon SageMaker Ground Truth is a data labeling service. It allows you to generate high-quality training datasets for machine learning models. The service offers two options: Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth. SageMaker Ground Truth Plus is a turnkey solution where the service takes care of everything from building labeling applications to providing and managing expert workforces so all you need to do is to provide your data along with labeling requirements. We will focus on SageMaker Ground Truth, where you can build and manage your own labeling workflows and workforces. Although not the focus of this chapter, SageMaker Ground Truth can also help you build labeled synthetic datasets – critical for use cases where acquiring real-world data is time-consuming and expensive.

Benefits of Amazon SageMaker Ground Truth

SageMaker Ground Truth provides a number of benefits when it comes to data labeling...