Computer vision on AWS : build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker / Lauren Mullennex, Nate Bachmeier, and Jay Rao.

Format:

Book

Author/Creator:

Mullennex, Lauren, author.

Bachmeier, Nate, author.

Rao, Jay, author.

Language:

English

Subjects (All):

Artificial intelligence.

Physical Description:

1 online resource (324 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2023]

Summary:

Develop scalable computer vision solutions for real-world business problems and discover scaling, cost reduction, security, and bias mitigation best practices with AWS AI/ML services Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn how to quickly deploy and automate end-to-end CV pipelines on AWS Implement design principles to mitigate bias and scale production of CV workloads Work with code examples to master CV concepts using AWS AI/ML services Book Description 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. What you will learn Apply CV across industries, including e-commerce, logistics, and media Build custom image classifiers with Amazon Rekognition Custom Labels Create automated end-to-end CV workflows on AWS Detect product defects on edge devices using Amazon Lookout for Vision Build, deploy, and monitor CV models using Amazon SageMaker Discover best practices for designing and evaluating CV workloads Develop an AI governance strategy across the entire machine learning life cycle Who this book is for If you are a machine learning engineer or data scientist looking to discover best practices and learn how to build comprehensive CV solutions on AWS, this book is for you. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Table of Contents

Preface

Part 1: Introduction to CV on AWS and Amazon Rekognition

Chapter 1: Computer Vision Applications and AWS AI/ML Services Overview

Technical requirements

Understanding CV

CV architecture and applications

Data processing and feature engineering

Data labeling

Solving business challenges with CV

Contactless check-in and checkout

Video analysis

Content moderation

CV at the edge

Exploring AWS AI/ML services

AWS AI services

Amazon SageMaker

Setting up your AWS environment

Creating an Amazon SageMaker Jupyter notebook instance

Summary

Chapter 2: Interacting with Amazon Rekognition

The Amazon Rekognition console

Using the Label detection demo

Examining the API request

Examining the API response

Other demos

Monitoring Amazon Rekognition

Quick recap

Detecting Labels using the API

Uploading the images to S3

Initializing the boto3 client

Detect the Labels

Using the Label information

Using bounding boxes

Cleanup

Chapter 3: Creating Custom Models with Amazon Rekognition Custom Labels

Introducing Amazon Rekognition Custom Labels

Benefits of Amazon Rekognition Custom Labels

Creating a model using Rekognition Custom Labels

Deciding the model type based on your business goal

Creating a model

Improving the model

Starting your model

Analyzing an image

Stopping your model

Building a model to identify Packt's logo

Step 1 - Collecting your images

Step 2 - Creating a project

Step 3 - Creating training and test datasets

Step 4 - Adding labels to the project

Step 5 - Drawing bounding boxes on your training and test datasets

Step 6 - Training your model.

Validating that the model works

Step 1 - Starting your model

Step 2 - Analyzing an image with your model

Step 3 - Stopping your model

Part 2: Applying CV to Real-World Use Cases

Chapter 4: Using Identity Verification to Build a Contactless Hotel Check-In System

Prerequisites

Creating the image bucket

Uploading the sample images

Creating the profile table

Introducing collections

Creating a collection

Describing a collection

Deleting a collection

Describing the user journeys

Registering a new user

Authenticating a user

Registering a new user with an ID card

Updating the user profile

Implementing the solution

Checking image quality

Indexing face information

Search existing faces

Supporting ID cards

Reading an ID card

Using the CompareFaces API

Guidance for identity verification on AWS

Solution overview

Deployment process

Chapter 5: Automating a Video Analysis Pipeline

Creating the video bucket

Uploading content to Amazon S3

Creating the person-tracking topic

Subscribing a message queue to the person-tracking topic

Creating the person-tracking publishing role

Setting up IP cameras

Using IP cameras

Installing OpenCV

Installing additional modules

Connecting with OpenCV

Viewing the frame

Uploading the frame

Reporting frame metrics

Using the PersonTracking API

Uploading the video to Amazon S3

Using the StartPersonTracking API

Receiving the completion notification

Using the GetPersonTracking API

Reviewing the GetPersonTracking response

Chapter 6: Moderating Content with AWS AI Services

Technical requirements.

Moderating images

Using the DetectModerationLabels API

Using top-level categories

Using secondary-level categories

Putting it together

Moderating videos

Creating the supporting resources

Finding the resource ARNs

Uploading the sample video to Amazon S3

Using the StartContentModeration API

Examining the completion notification

Using the GetContentModeration API

Using AWS Lambda to automate the workflow

Implement the Start Analysis Handler

Implementing the Get Results Handler

Publishing function changes

Experiment with the end-to-end

Part 3: CV at the edge

Chapter 7: Introducing Amazon Lookout for Vision

Introducing Amazon Lookout for Vision

The benefits of Amazon Lookout for Vision

Creating a model using Amazon Lookout for Vision

Choosing the model type based on your business goals

Building a model to identify damaged pills

Step 1 - collecting your images

Step 2 - creating a project

Step 3 - creating the training and test datasets

Step 4 - verifying the dataset

Step 5 - training your model

Validating it works

Step 1 - trial detection

Step 2 - starting your model

Step 3 - analyzing an image with your model

Step 4 - stopping your model

Chapter 8: Detecting Manufacturing Defects Using CV at the Edge

Understanding ML at the edge

Deploying a model at the edge using Lookout for Vision and AWS IoT Greengrass

Step 1 - Launch an Amazon EC2 instance

Step 2 - Create an IAM role and attach it to an EC2 instance

Step 3 - Install AWS IoT Greengrass V2

Step 4 - Upload training and test datasets to S3

Step 5 - Create a project.

Step 6 - Create training and test datasets

Step 7 - Train the model

Step 8 - Package the model

Step 9 - Configure IoT Greengrass IAM permissions

Step 10 - Deploy the model

Step 11 - Run inference on the model

Step 12 - Clean up resources

Part 4: Building CV Solutions with Amazon SageMaker

Chapter 9: Labeling Data with Amazon SageMaker Ground Truth

Introducing Amazon SageMaker Ground Truth

Benefits of Amazon SageMaker Ground Truth

Automated data labeling

Labeling Packt logos in images using Amazon SageMaker Ground Truth

Step 1 - collect your images

Step 2 - create a labeling job

Step 3 - specify the job details

Step 4 - specify worker details

Step 5 - providing labeling instructions

Step 6 - start labeling

Step 7 - output data

Importing the labeled data with Rekognition Custom Labels

Step 1 - create the project

Step 2 - create training and test datasets

Step 3 - model training

Chapter 10: Using Amazon SageMaker for Computer Vision

Fetching the LabelMe-12 dataset

Installing TensorFlow 2.0

Installing matplotlib

Using the built-in image classifier

Upload the dataset to Amazon S3

Prepare the job channels

Start the training job

Monitoring and troubleshooting

Handling binary metadata files

Declaring the Label class

Reading the annotations file

Declaring the Annotation class

Validate parsing the file

Restructure the files

Load the dataset

Part 5: Best Practices for Production-Ready CV Workloads

Chapter 11: Integrating Human-in-the-Loop with Amazon Augmented AI (A2I)

Introducing Amazon A2I

Core concepts of Amazon A2I

Learning how to build a human review workflow.

Creating a labeling workforce

Setting up an A2I human review workflow or flow definition

Initiating a human loop

Leveraging Amazon A2I with Amazon Rekognition to review images

Step 2 - Creating a work team

Step 3 - Creating a human review workflow

Step 4 - Starting a human loop

Step 5 - Checking the human loop status

Step 6 - Reviewing the output data

Chapter 12: Best Practices for Designing an End-to-End CV Pipeline

Defining a problem that CV can solve and processing data

Developing a CV model

Training

Evaluating

Tuning

Deploying and monitoring a CV model

Shadow testing

A/B testing

Blue/Green deployment strategy

Monitoring

Developing an MLOps strategy

SageMaker MLOps features

Workflow automation tools

Using the AWS Well-Architected Framework

Cost optimization

Operational excellence

Reliability

Performance efficiency

Security

Sustainability

Chapter 13: Applying AI Governance in CV

Understanding AI governance

Defining risks, documentation, and compliance

Data risks and detecting bias

Auditing, traceability, and versioning

Monitoring and visibility

MLOps

Responsibilities of business stakeholders

Applying AI governance in CV

Types of biases

Mitigating bias in identity verification workflows

Using Amazon SageMaker for governance

ML governance capabilities with Amazon SageMaker

Amazon SageMaker Clarify for explainable AI

Index

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Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781803248202

1803248203

OCLC:

1374423508