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Eliana Lam for AWS Community On Air

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Serverless MediaOps: Automating Video Workflows with AI on Amazon Web Services

Speaker: Luis Valdivia @ AWS Amarathon 2025

Summary by Amazon Nova



Problem Overview

  • Manual video processing

  • Slow turnaround time

  • Hard to scale or automate

  • Heavy ops / server maintenance

Traditional Video Workflow Summary

  • [ 1 ] Input: 

  • Content is manually managed through initial operations.

  • Manual tasks

  • Long processing time

  • Servers utilized

  • Transcoding backlog

  • [ 2 ] Operations Flow:

  • Input goes to a Cron Job (a scheduling utility).

  • The cron job triggers Encoding.

  • Metadata is generated and stored on EC2 Servers.

  • After encoding/storage, the content undergoes Content Review.

  • The reviewed content is then pushed to the audience.

  • [ 3 ] Output: 

  • The final consumption stage on a computer monitor, representing distribution.

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What is MediaOps?

  • MediaOps = DevOps for video workflows

  • Automates ingest → processing → delivery

  • Reduces manual steps

  • Ensures consistent, scalable pipelines

  • Improves quality, speed, and reliability

A four-step Media Operations (MediaOps) workflow:

  • Ingest: The process of taking in media content.

  • Process: The stage where media is prepared or modified.

  • Quality/Metadata: The step involving quality control and adding relevant data about the media.

  • Delivery: The final stage where the media is distributed or made available to its destination.

Core Amazon Web Services

  • S3 – ingest & storage

  • Lambda – event-driven logic

  • Step Functions – orchestration

  • MediaConvert – transcoding

  • Rekognition / Bedrock – analysis & AI metadata

  • CloudFront – global delivery



AI Automation Layer

  • Scene analysis (Rekognition)

  • Auto-generated metadata (Bedrock)

  • Intelligent decisions: reprocess, flag, publish

  • Event-driven orchestration (Lambda + Step Functions)

AI Automation Layer Workflow Summary

AI-driven video content workflow:

  • Input: A Video Output is directed into the automation system.

  • AI Automation: The core processing uses AI services, Rekognition and Bedrock.

  • Outputs/Actions: Based on the AI analysis, the system can trigger one of three actions:

  • [ 1 ] Reprocess: Send the content back for further processing.

  • [ 2 ] Flag: Mark the content for manual review or attention.

  • [ 3 ] Publish: Distribute the content live.

Key Benefits

  • Key benefits encompass eliminating 80% of manual operations

  • Accelerating publish time by 10 times

  • Achieving automatic scalability, enhancing discoverability and compliance with AI-generated consistent quality and metadata.



Team:

AWS FSI Customer Acceleration Hong Kong

AWS Amarathon Fan Club

AWS Community Builder Hong Kong

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