Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window. Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window.

Method: projects.locations.endpoints.deployModel

Deploys a Model into this Endpoint, creating a DeployedModel within it.

Endpoint

post


                https://{service-endpoint}/v1/{endpoint}:deployModel

Where {service-endpoint} is one of the supported service endpoints.

Path parameters

endpoint string

Required. The name of the Endpoint resource into which to deploy a Model. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Request body

The request body contains data with the following structure:

Fields

deployedModel object (DeployedModel)

Required. The DeployedModel to be created within the Endpoint. Note that Endpoint.traffic_split must be updated for the DeployedModel to start receiving traffic, either as part of this call, or via EndpointService.UpdateEndpoint.

trafficSplit map (key: string, value: integer)

A map from a DeployedModel's id to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel.

If this field is non-empty, then the Endpoint's trafficSplit will be overwritten with it. To refer to the id of the just being deployed Model, a "0" should be used, and the actual id of the new DeployedModel will be filled in its place by this method. The traffic percentage values must add up to 100.

If this field is empty, then the Endpoint's trafficSplit is not updated.

Response body

If successful, the response body contains an instance of Operation.