release engineering

Explores how to ship AI systems with the rigor of modern release engineering: data and model versioning, reproducible pipelines, gated evaluations, safety/bias checks, CI/CD for ML, and progressive delivery (canaries, feature flags, rollbacks). Stresses observability, incident response, documentation, and governance. Shows how cross‑functional collaboration turns prototypes into reliable, compliant, continuously improving products.