REST Resource: projects.models

Resource: Model

Represents a machine learning solution.

A model can have multiple versions, each of which is a deployed, trained model ready to receive prediction requests. The model itself is just a container.

JSON representation
{
  "name": string,
  "description": string,
  "defaultVersion": {
    object(Version)
  },
  "regions": [
    string
  ],
  "onlinePredictionLogging": boolean,
}
Fields
name

string

Required. The name specified for the model when it was created.

The model name must be unique within the project it is created in.

description

string

Optional. The description specified for the model when it was created.

defaultVersion

object(Version)

Output only. The default version of the model. This version will be used to handle prediction requests that do not specify a version.

You can change the default version by calling projects.methods.versions.setDefault.

regions[]

string

Optional. The list of regions where the model is going to be deployed. Currently only one region per model is supported. Defaults to 'us-central1' if nothing is set. Note: * No matter where a model is deployed, it can always be accessed by users from anywhere, both for online and batch prediction. * The region for a batch prediction job is set by the region field when submitting the batch prediction job and does not take its value from this field.

onlinePredictionLogging

boolean

Optional. If true, enables StackDriver Logging for online prediction. Default is false.

Methods

create

Creates a model which will later contain one or more versions.

delete

Deletes a model.

get

Gets information about a model, including its name, the description (if set), and the default version (if at least one version of the model has been deployed).

getIamPolicy

Gets the access control policy for a resource.

list

Lists the models in a project.

patch

Updates a specific model resource.

setIamPolicy

Sets the access control policy on the specified resource.

testIamPermissions

Returns permissions that a caller has on the specified resource.

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Cloud Machine Learning Engine (Cloud ML Engine)