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Computer vision on AWS build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[S.l.] :
PACKT PUBLISHING LIMITED,
2023.
|
| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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