ModelDeploymentMonitoringJob class - aiplatform library

Constructors

ModelDeploymentMonitoringJob(String name, {ModelDeploymentMonitoringJobArgs? args, CustomResourceOptions? options}): Creates a new ModelDeploymentMonitoringJob. name The Pulumi resource name. args Arguments used to configure this ModelDeploymentMonitoringJob. The set of arguments for ModelDeploymentMonitoringJob. options Resource options controlling this resource's behavior.

Properties

analysisInstanceSchemaUri ↔ Output<String>: YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
latefinal
bigqueryTables ↔ Output<List<Map<String, dynamic>>>: The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response
latefinal
childResources → Set<Resource>: finalinherited
completionSources ↔ Map<String, IOutputCompletionSource>: latefinalinherited
createTime ↔ Output<String>: Timestamp when this ModelDeploymentMonitoringJob was created.
latefinal
displayName ↔ Output<String>: The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
latefinal
enableMonitoringPipelineLogs ↔ Output<bool>: If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing.
latefinal
encryptionSpec ↔ Output<GoogleCloudAiplatformV1EncryptionSpecResponse>: Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.
latefinal
endpoint ↔ Output<String>: Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
latefinal
error ↔ Output<GoogleRpcStatusResponse>: Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
latefinal
hashCode → int: The hash code for this object.
no setterinherited
id ↔ Output<String>: getter/setter pairinherited
isCustom → bool: Returns whether this resource is provider-managed.
no setterinherited
isProtected → bool: Returns whether this resource is protected from deletion.
no setterinherited
isRemote → bool: Whether this resource is registered as remote.
no setterinherited
isResourceReference → bool: Whether this instance represents a resource value returned over RPC.
finalinherited
labels ↔ Output<Map<String, String>>: The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
latefinal
latestMonitoringPipelineMetadata ↔ Output<GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse>: Latest triggered monitoring pipeline metadata.
latefinal
location ↔ Output<String>: latefinal
loggingSamplingStrategy ↔ Output<GoogleCloudAiplatformV1SamplingStrategyResponse>: Sample Strategy for logging.
latefinal
logTtl ↔ Output<String>: The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.
latefinal
modelDeploymentMonitoringObjectiveConfigs ↔ Output<List<Map<String, dynamic>>>: The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
latefinal
modelDeploymentMonitoringScheduleConfig ↔ Output<GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfigResponse>: Schedule config for running the monitoring job.
latefinal
modelMonitoringAlertConfig ↔ Output<GoogleCloudAiplatformV1ModelMonitoringAlertConfigResponse>: Alert config for model monitoring.
latefinal
name ↔ Output<String>: Resource name of a ModelDeploymentMonitoringJob.
latefinal
nextScheduleTime ↔ Output<String>: Timestamp when this monitoring pipeline will be scheduled to run for the next round.
latefinal
predictInstanceSchemaUri ↔ Output<String>: YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.
latefinal
project ↔ Output<String>: latefinal
resourceTransforms → List<ResourceTransform>: Inherited/explicit async transforms.
no setterinherited
runtimeType → Type: A representation of the runtime type of the object.
no setterinherited
samplePredictInstance ↔ Output: Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
latefinal
scheduleState ↔ Output<String>: Schedule state when the monitoring job is in Running state.
latefinal
state ↔ Output<String>: The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.
latefinal
statsAnomaliesBaseDirectory ↔ Output<GoogleCloudAiplatformV1GcsDestinationResponse>: Stats anomalies base folder path.
latefinal
transformations → List<ResourceTransformation>: Inherited/explicit legacy transformations.
no setterinherited
updateTime ↔ Output<String>: Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
latefinal
urn ↔ Output<String>: latefinalinherited

Methods

failId(Object error) → void: Completes this resource ID with an error when registration fails.
inherited
failOutputs(Object error) → void: Completes all output properties with error.
inherited
failUrn(Object error) → void: Completes this resource URN with an error when registration fails.
inherited
getProvider(String moduleMember) → ProviderResource?: Returns provider for moduleMember's package, if configured.
inherited
getResourceName() → String: Returns this resource's logical name.
inherited
getResourceType() → String: Returns this resource's Pulumi type token.
inherited
noSuchMethod(Invocation invocation) → dynamic: Invoked when a nonexistent method or property is accessed.
inherited
registerOutput<T>(String propertyName, {Object? decoder(Object?)?, bool isSecret = false}) → Output<T>: Registers a dynamic output property for this resource.
inherited
resolveId(String? value, {required bool isKnown}) → void: Resolves the provider-assigned ID for this resource.
inherited
resolveOutputs(Struct outputs) → void: Resolves all output properties from a monitor response payload.
inherited
resolveUrn(String value) → void: Resolves this resource's URN once assigned by the engine.
inherited
serializeProperties(Map<String, dynamic> properties) → Future<Struct>: Serializes resource properties for RPC transmission.
inherited
toString() → String: A string representation of this object.
inherited

Operators

operator ==(Object other) → bool: The equality operator.
inherited