What Is Deployment?

Deployment is the step of putting a trained AI model into live use, where it responds to real requests from users or other systems. It moves the model out of the lab and into production, connecting it to the applications, data, and infrastructure it needs to serve predictions reliably, at the required speed and scale.

How deployment works

Deployment works by wrapping a trained model in the infrastructure it needs to run in the real world. The model is packaged, placed on a server or cloud service, and given an interface, usually an API, that lets applications send it inputs and receive results. This live, request-answering stage is called inference, as opposed to training, which happened earlier.

Deployment does not end at launch. A model in production is monitored for speed, cost, and accuracy, and gets updated or retrained as the data shifts over time. That ongoing work is often grouped under the term MLOps.

Why deployment matters for business

A model delivers no value until it is deployed, since high accuracy on a test set means nothing if nothing can call the model. Deployment is where AI stops being a project and starts doing work. It is also where many efforts stall, because production brings demands that training never did: reliability, security, latency, and cost at scale. At Custom AI Studio, we build for deployment from the start, so the systems we hand over run dependably in production rather than only in a demo.