Week 5: Shipping Systems, Not Demos

This lesson introduces containers as the simplest practical tool for environment reproducibility and service packaging.

AI stacks often involve multiple services, provider SDKs, env vars, and background jobs. Without reproducibility, debugging and deployment become guesswork plus tribal memory.

A container captures enough runtime context to make an application portable and predictable. It is not a full platform, but it is a disciplined contract around execution.

This lesson focuses on deployment architecture as an explainable system, not a pile of platform defaults.

When deploy paths are unclear, incidents become harder to diagnose and recovery becomes improvisation. AI systems suffer more because they already have more moving parts than ordinary CRUD apps.

A good deployment path states what runs where, what environment each component needs, how changes are released, and how failures are rolled back.

This lesson is about hardening the operational perimeter of an AI service: privileges, network exposure, secrets, and scanning habits.

A surprisingly large fraction of breaches and production failures come from default-open thinking. AI features do not change that rule; they often make the consequences worse.

Every credential, open route, storage bucket, and admin action is a capability. Least privilege means capabilities exist only where required, for as little time and as small a surface as possible.