Building Your Own Video Processing Pipeline

Component Checklist

Infrastructure

• ☐ GPU instances (cloud or on-premise)
• ☐ Autoscaling configuration
• ☐ Job queue (Redis, SQS, RabbitMQ)
• ☐ Object storage (S3 or equivalent)
• ☐ Database (job metadata)
• ☐ Load balancer
• ☐ Monitoring stack

Software

• ☐ Worker application (GPU processing)
• ☐ API service (job submission)
• ☐ Webhook service (notifications)
• ☐ Video decode/encode (FFmpeg)
• ☐ AI model integration
• ☐ Error handling and retry
• ☐ Logging and tracing

Operations

• ☐ Deployment pipeline (CI/CD)
• ☐ Monitoring dashboards
• ☐ Alerting rules
• ☐ On-call rotation
• ☐ Runbooks for incidents
• ☐ Backup and recovery

Security

• ☐ Authentication (API keys, tokens)
• ☐ Authorization (access control)
• ☐ Encryption (transit and rest)
• ☐ Audit logging
• ☐ Penetration testing

Skills Required

Essential Skills

• GPU/CUDA: Running AI models on GPU
• ML Ops: Model deployment and optimization
• Video: Codecs, FFmpeg, streaming
• Distributed Systems: Queues, workers, scaling
• DevOps: Cloud infrastructure, containers

Team Composition

Minimum viable team:

• 1 ML/GPU engineer (model and inference)
• 1 Backend engineer (API and infrastructure)
• 0.5 DevOps engineer (cloud and operations)

Larger teams add:

• Security engineer
• Video specialist
• Site reliability engineer

Hiring Challenge

GPU/ML engineers are:

• Expensive ($200K-$400K total compensation)
• Scarce (high demand from AI companies)
• Picky (want interesting problems)

Realistic Timeline

Phase 1: Prototype (4-6 weeks)

• Single-machine processing
• Basic API
• "Works on my machine"

Phase 2: MVP (6-10 weeks)

• Job queue and workers
• Basic scaling
• Happy path works

Phase 3: Production (8-12 weeks)

• Error handling
• Monitoring and alerting
• Security hardening
• Load testing

Total: 4-7 months

This is best case with experienced team. Common delays:

• GPU availability issues
• Model performance optimization
• Edge case handling
• Integration problems
• Hiring delays

Common Mistakes

Underestimating Video

Video is harder than images:

• File sizes 100-1000x larger
• Processing time 100x longer
• More edge cases (codecs, framerates, corruption)

Ignoring Operations

Building is 30% of the work. Operating is 70%:

• What happens when the queue backs up?
• What happens when a worker crashes mid-job?
• What happens when GPUs run out of memory?

Underbudgeting GPU

Common mistake: "We'll use T4s, they're cheap."

• T4s are too slow for production video
• A10G is minimum for reasonable throughput
• 4K requires L40S or tiling

Skipping Compliance

If you need HIPAA/SOC 2 later, retrofitting is painful:

• Audit logs need to be comprehensive
• Access controls need to be tight
• Documentation needs to exist