Computer vision on AWS build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker

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Average Rating
Published
[S.l.] : PACKT PUBLISHING LIMITED, 2023.
Status
Available Online

Description

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.

More Details

Format
Edition
1st edition.
Language
English
ISBN
9781803248202, 1803248203

Notes

Description
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.
Local note
O'Reilly O'Reilly Online Learning: Academic/Public Library Edition

Table of 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
Technical requirements
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
Quick recap
Cleanup
Summary
Chapter 3: Creating Custom Models with Amazon Rekognition Custom Labels
Technical requirements
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
Summary
Part 2: Applying CV to Real-World Use Cases
Chapter 4: Using Identity Verification to Build a Contactless Hotel Check-In System
Technical requirements
Prerequisites
Creating the image bucket
Uploading the sample images
Creating the profile table
Introducing collections
Creating a collection
Describing a collection
Deleting a collection
Quick recap
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
Quick recap
Supporting ID cards
Reading an ID card
Using the CompareFaces API
Quick recap
Guidance for identity verification on AWS
Solution overview
Deployment process
Cleanup
Summary

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Citations

APA Citation, 7th Edition (style guide)

Mullennex, L., Bachmeier, N., & Rao, J. (2023). Computer vision on AWS: build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker (1st edition.). PACKT PUBLISHING LIMITED.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Mullennex, Lauren, Nate, Bachmeier and Jay, Rao. 2023. Computer Vision On AWS: Build and Deploy Real-world CV Solutions With Amazon Rekognition, Lookout for Vision, and SageMaker. [S.l.]: PACKT PUBLISHING LIMITED.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Mullennex, Lauren, Nate, Bachmeier and Jay, Rao. Computer Vision On AWS: Build and Deploy Real-world CV Solutions With Amazon Rekognition, Lookout for Vision, and SageMaker [S.l.]: PACKT PUBLISHING LIMITED, 2023.

Harvard Citation (style guide)

Mullennex, L., Bachmeier, N. and Rao, J. (2023). Computer vision on AWS: build and deploy real-world CV solutions with amazon rekognition, lookout for vision, and sagemaker. 1st edn. [S.l.]: PACKT PUBLISHING LIMITED.

MLA Citation, 9th Edition (style guide)

Mullennex, Lauren,, Nate Bachmeier, and Jay Rao. Computer Vision On AWS: Build and Deploy Real-world CV Solutions With Amazon Rekognition, Lookout for Vision, and SageMaker 1st edition., PACKT PUBLISHING LIMITED, 2023.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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Grouped Work ID64d69e32-cbf5-b253-ad2b-a5d4aab9c548-eng
Full titlecomputer vision on aws build and deploy real world cv solutions with amazon rekognition lookout for vision and sagemaker
Authormullennex lauren
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Last Update2025-02-11 03:40:45AM
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5050 |a 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
5058 |a Setting up your AWS environment -- Creating an Amazon SageMaker Jupyter notebook instance -- Summary -- Chapter 2: Interacting with Amazon Rekognition -- Technical requirements -- 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 -- Quick recap -- Cleanup -- Summary
5058 |a Chapter 3: Creating Custom Models with Amazon Rekognition Custom Labels -- Technical requirements -- 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
5058 |a 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 -- Summary -- Part 2: Applying CV to Real-World Use Cases -- Chapter 4: Using Identity Verification to Build a Contactless Hotel Check-In System -- Technical requirements -- Prerequisites -- Creating the image bucket -- Uploading the sample images -- Creating the profile table -- Introducing collections
5058 |a Creating a collection -- Describing a collection -- Deleting a collection -- Quick recap -- 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 -- Quick recap -- Supporting ID cards -- Reading an ID card -- Using the CompareFaces API -- Quick recap -- Guidance for identity verification on AWS -- Solution overview -- Deployment process -- Cleanup -- Summary
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