What does this AWS Solution do?

The Media2Cloud 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.

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Save development time

Leverage the Media2Cloud 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.

Simple user interface

The solution creates a simple web interface that makes it easy to upload, browse, and search your video and image files and extracted metadata.

AWS Solution overview

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.

Media2Cloud solution architecture

The AWS CloudFormation template deploys the following infrastructure:

1. An Amazon Cognito user pool to provide a user directory.

2. An Amazon API Gateway RESTful API endpoint, which is configured to use AWS IAM authentication.

3. An Amazon CloudFront distribution that hosts the web application artifacts such as minimized JavaScript files and graphics stored in the web bucket.

4. An AWS StepFunctions main state machine which serves as the entry point to the solution’s backend ingestion and analysis workflows.

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.

6. An AWS 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.

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.

8. An Amazon OpenSearch Service cluster, which stores ingestion attributes and machine learning metadata, and facilitates customers’ search and discovery needs.

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.

10. Amazon CloudWatch event rules that are logged when specific tasks undergo state changes.

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.

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.

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.

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