google_api_machine_learning v0.3.0 GoogleApi.MachineLearning.V1.Model.GoogleCloudMlV1Model View Source
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.
Attributes
- defaultVersion (GoogleCloudMlV1Version): 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. Defaults to:
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. - description (String.t): Optional. The description specified for the model when it was created. Defaults to:
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. - etag (binary()): `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 `GetModel`, and systems are expected to put that etag in the request to `UpdateModel` to ensure that their change will be applied to the model as intended. Defaults to:
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. - labels (%{optional(String.t) => String.t}): 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 <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>. Defaults to:
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. - name (String.t): Required. The name specified for the model when it was created. The model name must be unique within the project it is created in. Defaults to:
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. - onlinePredictionLogging (boolean()): Optional. If true, enables StackDriver Logging for online prediction. Default is false. Defaults to:
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. - regions ([String.t]): 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 <a href="/ml-engine/docs/tensorflow/regions">available regions</a> 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. Defaults to:
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Functions
Unwrap a decoded JSON object into its complex fields
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decode(value, options) View Source
Unwrap a decoded JSON object into its complex fields.