Deployment is the point at which an AI system goes live for real users. Whether integrated into a customer-facing application, an internal workflow tool, or a background processing pipeline, deployment marks the transition from development and testing into production.
Deployment is where performance and reliability truly matter, and where the gap between controlled testing conditions and real-world variability becomes visible. A system that performed well in testing will frequently encounter situations it was not explicitly tested against — which is why monitoring, fallback mechanisms, and clear escalation paths matter from day one of production.
