CI/CD Basics for AI Apps
Explore CI CD basics for AI applications focusing on building reproducible Docker containers, implementing secure secret management, and automating testing for stable deployments. Understand scaling constraints specific to LLMs and how to design pipelines for consistent environments, reliable health checks, and safe rollbacks across development stages.
A FastAPI LLM service can return the same JSON on a laptop and still fail after a commit lands because secrets come from a different place, outbound networking is restricted, and streaming connections hold resources longer than a quick request. The boundary to design for is not code correctness alone, but repeatability across machines and environments while keeping provider credentials out of the image and out of git.
This lesson stays anchored on a minimal Docker-to-AWS deploy path with CI checks that gate on tests and evals, plus scaling constraints that reflect provider quotas and streaming concurrency.
Before we touch any config, lock the constraints in writing so each later choice has a reason. We want a reproducible build, safe secret injection, separation across dev, stage, and prod, and a rollback path that lets us re-run yesterday’s release even if the main branch changed. LLM specifics add provider credentials, outbound egress to provider endpoints, and long-lived streaming connections that change how we set timeouts and concurrency. ...