Serving is the running system that answers prediction requests. Inference is the single act of computing one prediction from one input. Deployment is the release event that puts a specific model version behind the serving system.
The distinction matters when something breaks. Inference is a math problem you can reproduce in a notebook. Serving is an availability problem with queues, timeouts, autoscaling, and a p99 latency number. Deployment is a change problem with rollback, approval, and a record of who shipped what.
Teams blur the three and then own the wrong thing. A model that scores well offline still needs capacity planning, warm-up, and a health check before it can carry traffic. A perfectly healthy service can also serve a stale model for months. That happens when nobody treated deployment as its own step with its own audit trail.
This answer doesn't lend itself to a diagram - it reads best . No credits were charged.
Why there's no diagram: “”
The interactive diagram is below the answer - jump to diagram ↓ · Below it, the related concept . Jump to it ↓
The diagram below the answer is the concept . Jump to it ↓