Cloud Deployment for AI Applications

Define API, storage and inference responsibilities.

Evidence: A component diagram records interfaces and failure points.

Build typed request validation and consistent errors.

Evidence: Invalid inputs do not reach inference execution.

Create a reproducible image with a non-root runtime user.

Evidence: The service runs from a fresh image with no embedded secrets.

Separate public settings from short-lived credentials.

Evidence: A scan finds no secrets in source, image or logs.

Map compute, network, storage and managed identity to a deployment.

Evidence: An architecture identifies public and private boundaries.

Provision a local or approved cloud environment from versioned configuration.

Evidence: A repeat run produces no unintended duplicate resources.

Build, test and publish a tagged image after checks.

Evidence: A failed test prevents deployment.

Implement readiness checks and load a bounded workload.

Evidence: Unhealthy instances receive no new work.

Track request rate, errors, latency and resource use.

Evidence: A seeded failure produces a useful alert and trace.

Apply quotas, expiry and teardown to training infrastructure.

Evidence: An idle environment is stopped and its spend estimate recorded.

Deploy a faulty version and restore the previous release.

Evidence: A timed recovery preserves valid stored data.

Write service ownership, incident and maintenance instructions.

Evidence: A second operator can diagnose and recover a seeded outage.