Echonos AI Music Video Platform
Senior DevOps Engineer · Release Engineering & AI
A repeatable staging to production release path for an AI platform that turns songs into beat-synced music videos.
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The challenge
Echonos turns an uploaded song into a beat-synced, story-driven music video. Behind that single button sits a long-running generation pipeline: audio analysis, scene planning, video generation through external model providers, and final assembly, plus a web studio where artists edit the result. Shipping changes to a system like that is risky. A careless deploy can break generations that are already in flight, and the web app, the Python pipeline and the webhook services all have to move together without drifting apart.
Architecture
The product is a Next.js web app (Studio, Vault, Characters) deployed on Vercel, backed by Firebase Auth and Firestore, with media stored in Google Cloud Storage. Video generation runs through separate Python backend services: the generation pipeline, a template pipeline, and webhook handlers that receive results from model providers such as FAL, plus studio and export functions. Staging and production are fully separate environments, and each git branch maps to exactly one of them.
My role and how it shipped
As Senior DevOps Engineer I own release engineering. Deploys are branch aware: the current branch decides whether a service goes to staging or production, so nobody hand-writes cloud commands during a release. Each backend service deploys on its own, so a webhook fix does not force a pipeline redeploy. Changes are promoted from staging to production through one path, with Playwright smoke and verification runs on both sides of the promotion, a documented rollback, a hotfix lane for urgent fixes, and a written incident runbook.
Results
Releases now follow one predictable route from branch to staging to production. Smoke checks run before and after every promotion, runtime drift between environments is checked instead of assumed, and a bad release can be rolled back through a known procedure instead of a late-night improvisation.
What I learned
The web app and the generation pipeline fail in completely different ways, so they need different release gates. Environment parity is the thing most worth protecting: most of the scary bugs in an AI pipeline come from staging and production quietly disagreeing about configuration, not from the model itself.
Written by Mudassir Khan
CEO of Cube A Cloud (US) · Senior DevOps Engineer at Echonos AI · Web3 trainer · Islamabad, Pakistan
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