CI/CD Basics for AI Apps
Explore how to design and deploy production-ready AI applications using CI/CD pipelines. Understand Docker containerization, environment-specific configurations, secure secret management, and scaling constraints for large language model services. Gain practical knowledge to ensure reliable builds, automated testing, and smooth AWS deployments.
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. ...