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 Computer Vision

Amazon Rekognition is a purpose-built computer vision service for 80% of everyday use cases. But what if you need more control and influence over that remaining 20%? In that case, you’d choose tooling from a lower level of AWS AI Layered Cake. These situations are precisely where Amazon SageMaker shines:

Figure 10.1 – AWS AI Layered Cake

Figure 10.1 – AWS AI Layered Cake

Amazon SageMaker aims to make machine learning (ML) possible for every developer, business analyst, and data scientist. It achieves this goal through a fully managed suite of services and tools that address each ML model life cycle management step. You can start building with fully managed Juypter notebooks and train your model at scale using elastic resource pools. Suppose you need features for data labeling, data wrangling, and creating human-in-the-loop (HITL) workflows. In that case, Amazon SageMaker GroundTruth, Data Wrangler, and Augmented AI (A2I) have you...