Deployment is the process of taking a fully trained AI model out of development and making it operational in a live environment where it can actually be used, whether that means integrating it into an existing application, exposing it through an API for other developers to call, or embedding it directly into a product's user-facing features.
Deployment involves more than just making a model technically reachable: it typically requires setting up infrastructure to handle real user traffic reliably, monitoring the model's performance and outputs once it's live, and having a plan to update or roll back the model if issues surface after release. For products like ChatGPT, deployment also means scaling that infrastructure to handle enormous request volume from millions of users simultaneously, which is a substantially different engineering challenge than getting a model to work correctly in a research or testing environment.
