Local to Live: The Complete Deployment Chain for an AI Project
Deployment is where many AI projects stall, and this guide names the exact steps and pitfalls, such as certificate-before-domain and exposed database ports. A developer who follows it gets a production-like setup with TLS, container isolation, and persistent data without having to reconstruct the process from scattered resources.
Deployment for a locally running AI service is a fixed sequence with traps hidden in the ordering. A cloud server comes first, then a domain with DNS pointing at its IP. The HTTPS certificate can only be issued after the domain exists, because the certificate binds to the domain name. In mainland China, that domain also needs ICP filing before it can serve public traffic.
Once infrastructure is up, the project services get containerized. The example AI stack runs FastAPI, MySQL, Redis, MinIO, and Qdrant. Docker Compose writes them all into one file and starts everything with a single command. Containers live on a private network; only Nginx exposes ports, so databases and caches never face the public internet. Named volumes keep data on the host disk, so container deletions don't erase user records or vector indexes.
The deployment itself follows a checklist: ignore build caches, build the frontend to dist, write a .env, upload code, run `docker compose up -d --build`, then run Alembic migrations inside the backend container. Nginx terminates TLS, serves static files, and proxies API calls to the backend, all through one gateway.
The certificate-after-domain rule is a classic dead end; starting certificate application before owning the domain wastes time and blocks every subsequent step.
Docker's internal network turns containerization into a security boundary, not just a reproducibility tool; the database never gains a public address.
An AI project with a vector store and object storage requires multiple services, making manual server setup error-prone; orchestration is effectively mandatory.