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Version: 23.09.05


A Deployment represents an instantiation of a machine learning model that is hosted on physical hardware. It serves as a reference to the actual deployment taking place on the underlying infrastructure. Deployments always include a model, and can optionally also host an explainer, transformer, or both. Each Deployment is equipped with an endpoint, enabling communication with the model to retrieve predictions and explanations.


Every Deployment has an owner, who assumes responsibility for its management. Initially, the user creating the deployment automatically becomes its owner.

Interact with a Deployment using the Deployment API, Python Client, or UI.