Overview
The Media2Cloud on AWS solution sets up a serverless end-to-end ingest workflow to move your video assets and associated metadata to the cloud. This solution is designed to provide a standardized architecture that helps accelerate the migration and supply chain process, and helps Media Asset Manager (MAM) partners provide solutions for their customers.
Benefits
Leverage the Media2Cloud on AWS solution out of-the-box, or as a reference implementation for building a serverless framework to accelerate the setup and configuration of a content ingest process.
The solution creates a simple web interface that makes it easy to upload, browse, and search your video and image files and extracted metadata.
Technical details
This solution helps you to quickly and seamlessly extract key details from your media files in your AWS accounts without machine learning expertise. The solution also includes a web-based user interface that you can use to ingest and analyze your media content.
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 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 solution’s backend ingestion and analysis workflows.
Step 5
An AWS 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 AWS 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 customers’ search and discovery needs.
Step 9
Four Amazon Simple Storage Service (Amazon S3) buckets to store uploaded content, file proxies that the solution generates during ingestion, static web application artifacts, and access logs for services used.
Step 11
Amazon EventBridge used by an internal queue management system where the backlog system notifies workflows (state machines) when a queued 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/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 customers’ system.
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 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 solution’s backend ingestion and analysis workflows.
Step 5
An AWS 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 AWS 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 customers’ search and discovery needs.
Step 9
Four Amazon Simple Storage Service (Amazon S3) buckets to store uploaded content, file proxies that the solution generates during ingestion, static web application artifacts, and access logs for services used.
Step 11
Amazon EventBridge used by an internal queue management system where the backlog system notifies workflows (state machines) when a queued 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/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 customers’ system.
Related content
By engaging TrackIt to deploy the AWS Media2Cloud on AWS implementation, Jukin Media collated huge volumes of user-generated video content with a streamlined and automated process, saving them time and money.
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