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

go here Computer Vision on AWS Contributors About the authors About the reviewer Preface Who this book is for What this book covers To get the most out of this book Download the example code files Conventions used Get in touch Share Your Thoughts Download a free PDF copy of this book 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 Chapter 5: Automating a Video Analysis Pipeline Technical requirements 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 Quick recap Using IP cameras Installing OpenCV Installing additional modules Connecting with OpenCV Viewing the frame Uploading the frame Reporting frame metrics Quick recap 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 Viewing the frame Quick recap Summary 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 Quick recap 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 Quick recap 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 Summary Part 3: CV at the edge Chapter 7: Introducing Amazon Lookout for Vision Technical requirements 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 Creating a model Starting your model Analyzing an image Stopping your model 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 Summary Chapter 8: Detecting Manufacturing Defects Using CV at the Edge Technical requirements 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 Summary Part 4: Building CV Solutions with Amazon SageMaker Chapter 9: Labeling Data with Amazon SageMaker Ground Truth Technical requirements 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 Summary Chapter 10: Using Amazon SageMaker for Computer Vision Technical requirements 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 Quick recap 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 Quick recap Summary Part 5: Best Practices for Production-Ready CV Workloads Chapter 11: Integrating Human-in-the-Loop with Amazon Augmented AI (A2I) Technical requirements 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 1 – Collecting your 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 Summary 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 Summary 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 Summary Index Why subscribe? 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