How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Step 1
An Amazon Cognito user pool to provide a user directory.
Step 2
An Amazon API Gateway RESTful API endpoint, which is configured to use AWS Identity and Access Management (IAM) authentication.
Step 3
An Amazon CloudFront distribution that hosts the web application artifacts, such as minimized JavaScript files and graphics stored in the web bucket.
Step 4
An AWS Step Functions main state machine which serves as the entry point to the backend ingestion and analysis workflows.
Step 5
An Step Functions ingestion sub-state machine that orchestrates the ingestion process by media file type and generates proxies for ingested media. It uses AWS Elemental MediaConvert for video and audio files and open-source tools for image files and documents.
Step 6
A Step Functions analysis sub-state machine that is responsible for the analysis process. It consists of Step Functions that run analysis jobs with Amazon Rekognition, Amazon Transcribe, Amazon Comprehend, and Amazon Textract.
Step 7
Amazon DynamoDB tables to store artifacts generated during the ingestion and analysis processes, such as overall status, pointers to where intermediate files are stored, and state machine run tokens.
Step 8
An Amazon OpenSearch Service cluster, which stores ingestion attributes and machine learning metadata, and facilitates your search and discovery needs.
Step 9
Four Amazon Simple Storage Service (Amazon S3) buckets store: uploaded content, file proxies that the Guidance generates during ingestion, static web application artifacts, and access logs for services used.
Step 10
Amazon CloudWatch event rules that are logged when specific tasks undergo state changes.
Step 11
Amazon EventBridge used by an internal queue management system where the backlog system notifies workflows (state machines) when a queued artificial intelligence and machine learning (AI/ML) request has been processed.
Step 12
An AWS IoT Core topic that allows the ingestion and analysis workflows to communicate with the front-end web application asynchronously through publish or subscribe MQTT messaging.
Step 13
Amazon Simple Notification Service (Amazon SNS) topics to allow Amazon Rekognition to publish job status in the video analysis workflow, and to support custom integration with your system.