Interface ExplainRequestOrBuilder (3.56.0)

Optional. This field is the same as the one above, but supports multiple explanations to occur in parallel. The key can be any string. Each override will be run against the model, then its explanations will be grouped together.

Note - these explanations are run In Addition to the default Explanation in the deployed model.

map<string, .google.cloud.aiplatform.v1beta1.ExplanationSpecOverride> concurrent_explanation_spec_override = 6 [(.google.api.field_behavior) = OPTIONAL];

Use #getConcurrentExplanationSpecOverrideMap() instead.

Optional. This field is the same as the one above, but supports multiple explanations to occur in parallel. The key can be any string. Each override will be run against the model, then its explanations will be grouped together.

Note - these explanations are run In Addition to the default Explanation in the deployed model.

map<string, .google.cloud.aiplatform.v1beta1.ExplanationSpecOverride> concurrent_explanation_spec_override = 6 [(.google.api.field_behavior) = OPTIONAL];

Optional. This field is the same as the one above, but supports multiple explanations to occur in parallel. The key can be any string. Each override will be run against the model, then its explanations will be grouped together.

Note - these explanations are run In Addition to the default Explanation in the deployed model.

map<string, .google.cloud.aiplatform.v1beta1.ExplanationSpecOverride> concurrent_explanation_spec_override = 6 [(.google.api.field_behavior) = OPTIONAL];

Optional. This field is the same as the one above, but supports multiple explanations to occur in parallel. The key can be any string. Each override will be run against the model, then its explanations will be grouped together.

Note - these explanations are run In Addition to the default Explanation in the deployed model.

map<string, .google.cloud.aiplatform.v1beta1.ExplanationSpecOverride> concurrent_explanation_spec_override = 6 [(.google.api.field_behavior) = OPTIONAL];

Optional. This field is the same as the one above, but supports multiple explanations to occur in parallel. The key can be any string. Each override will be run against the model, then its explanations will be grouped together.

Note - these explanations are run In Addition to the default Explanation in the deployed model.

map<string, .google.cloud.aiplatform.v1beta1.ExplanationSpecOverride> concurrent_explanation_spec_override = 6 [(.google.api.field_behavior) = OPTIONAL];

If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split.

string deployed_model_id = 3;

If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split.

string deployed_model_id = 3;

Required. The name of the Endpoint requested to serve the explanation. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

string endpoint = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }

Required. The name of the Endpoint requested to serve the explanation. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

string endpoint = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }

If specified, overrides the explanation_spec of the DeployedModel. Can be used for explaining prediction results with different configurations, such as:

• Explaining top-5 predictions results as opposed to top-1;
• Increasing path count or step count of the attribution methods to reduce approximate errors;
• Using different baselines for explaining the prediction results.

.google.cloud.aiplatform.v1beta1.ExplanationSpecOverride explanation_spec_override = 5;

If specified, overrides the explanation_spec of the DeployedModel. Can be used for explaining prediction results with different configurations, such as:

• Explaining top-5 predictions results as opposed to top-1;
• Increasing path count or step count of the attribution methods to reduce approximate errors;
• Using different baselines for explaining the prediction results.

.google.cloud.aiplatform.v1beta1.ExplanationSpecOverride explanation_spec_override = 5;

Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value instances = 2 [(.google.api.field_behavior) = REQUIRED];

Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value instances = 2 [(.google.api.field_behavior) = REQUIRED];

Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value instances = 2 [(.google.api.field_behavior) = REQUIRED];

Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value instances = 2 [(.google.api.field_behavior) = REQUIRED];

Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value instances = 2 [(.google.api.field_behavior) = REQUIRED];

The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri.

.google.protobuf.Value parameters = 4;

The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri.

.google.protobuf.Value parameters = 4;

If specified, overrides the explanation_spec of the DeployedModel. Can be used for explaining prediction results with different configurations, such as:

• Explaining top-5 predictions results as opposed to top-1;
• Increasing path count or step count of the attribution methods to reduce approximate errors;
• Using different baselines for explaining the prediction results.

.google.cloud.aiplatform.v1beta1.ExplanationSpecOverride explanation_spec_override = 5;

The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri.

.google.protobuf.Value parameters = 4;