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

Creating a model using Rekognition Custom Labels

In this section, we will learn how to select the type of model you need for your use case, how to create training and test datasets, how to start model training, and how to analyze images using the trained model.

Deciding the model type based on your business goal

First, you should decide which type of model you want to train depending on your business goals. For example, you could train a model to find your logo in social media posts, identify your products on store shelves, or classify machine parts in an assembly line.

You can train the following types of models with Amazon Rekognition Custom Labels:

  • Object classification: to find objects, scenes, and concepts—With this type of model, you predict objects, scenes, or concepts associated and represented in the entire image. It is also referred to as image classification. For example, you can train a model that identifies the type of fruit in an image, such as...