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,
  "labels": {
    string: string,
    ...
  },
  "etag": string
}
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. See the available regions for ML Engine services. 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.

labels

map (key: string, value: string)

Optional. One or more labels that you can add, to organize your models. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.

An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.

etag

string (bytes format)

etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a model from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform model updates in order to avoid race conditions: An etag is returned in the response to models.get, and systems are expected to put that etag in the request to models.patch to ensure that their change will be applied to the model as intended.

A base64-encoded string.

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)