Guided Demo

Async AI Inference API Demo

What It Demonstrates

A simulated long-running AI inference workflow: submit job, queue, process, store result, check status, and receive callback. Shows the architecture pattern behind production async inference backends.

Who It Is For

Teams building model-serving APIs, image processing systems, batch inference, or AI backends.

Demo Flow

1. Submit a job via API form (upload input or provide parameters)
2. Job enters queue, status changes to "processing"
3. Worker processes the job (simulated or real inference)
4. Result stored, status changes to "completed"
5. User checks status endpoint or receives webhook callback
6. Display result with timing information

Architecture

User -> Frontend -> API -> Queue -> Worker -> Storage -> Status / Callback

Tech Stack

FastAPI, queue workers, Redis, RabbitMQ, object storage, Docker, webhooks.

Productionization Notes

• Queue design: Decoupling submission from processing for scalability
• Result storage: Object storage (S3/GCS) to avoid database bloat from large inference outputs
• Callbacks: Webhook delivery for real-time result notification to integrating systems
• Production concerns: Auth, monitoring, retries, error handling, rate limits, job evaluation, scaling, cost controls

CTA

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