Azure.ResourceManager.MachineLearning.Models Namespace

This connection type covers the AAD auth for any applicable Azure service.

The AccessKeyAuthTypeWorkspaceConnectionProperties.

This connection type covers the account key connection for Azure storage.

An Azure Machine Learning compute.

Compute node information related to a AmlCompute.

AML Compute properties.

scale settings for AML Compute.

AML Token identity configuration.

AML token compute identity definition.

Access credential with no credentials.

This connection type covers the generic ApiKey auth connection categories, for examples: AzureOpenAI: Category:= AzureOpenAI AuthType:= ApiKey (as type discriminator) Credentials:= {ApiKey} as Microsoft.MachineLearning.AccountRP.Contracts.WorkspaceConnection.ApiKey Target:= {ApiBase} CognitiveService: Category:= CognitiveService AuthType:= ApiKey (as type discriminator) Credentials:= {SubscriptionKey} as Microsoft.MachineLearning.AccountRP.Contracts.WorkspaceConnection.ApiKey Target:= ServiceRegion={serviceRegion} CognitiveSearch: Category:= CognitiveSearch AuthType:= ApiKey (as type discriminator) Credentials:= {Key} as Microsoft.MachineLearning.AccountRP.Contracts.WorkspaceConnection.ApiKey Target:= {Endpoint} Use Metadata property bag for ApiType, ApiVersion, Kind and other metadata fields

A factory class for creating instances of the models for mocking.

The AutoDeleteSetting.

Forecast horizon determined automatically by system.

AutoMLJob class. Use this class for executing AutoML tasks like Classification/Regression etc. See TaskType enum for all the tasks supported.

AutoML vertical class. Base class for AutoML verticals - TableVertical/ImageVertical/NLPVertical Please note this is the abstract base class. The derived classes available for instantiation are: ClassificationTask, MachineLearningForecasting, ImageClassification, ImageClassificationMultilabel, ImageInstanceSegmentation, ImageObjectDetection, AutoMLVerticalRegression, TextClassification, TextClassificationMultilabel, and TextNer.

Regression task in AutoML Table vertical.

N-Cross validations determined automatically.

The AutoSeasonality.

The AutoTargetLags.

Target lags rolling window determined automatically.

The AzMonMonitoringAlertNotificationSettings.

Webhook details specific for Azure DevOps.

Azure ML batch inferencing server configurations.

Azure ML online inferencing configurations.

Defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation.

The BaseEnvironmentSource. Please note BaseEnvironmentSource is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include BaseEnvironmentType.

Base environment type.

Properties relevant to different deployment types. Please note this is the abstract base class. The derived classes available for instantiation are: BatchPipelineComponentDeploymentConfiguration.

Properties for a Batch Pipeline Component Deployment.

Defines a Sampling Algorithm that generates values based on previous values.

The BlobReferenceForConsumptionDto.

BlobReferenceSASRequest for getBlobReferenceSAS API.

BlobReferenceSASResponse for getBlobReferenceSAS API.

The CapabilityHostProperties.

The CategoricalDataDriftMetricThreshold.

The CategoricalDataQualityMetricThreshold.

The CategoricalPredictionDriftMetricThreshold.

The ClassificationModelPerformanceMetricThreshold.

Classification task in AutoML Table vertical.

Classification Training related configuration.

The CocoExportSummary.

Column transformer parameters.

The workflow trigger cron for ComputeStartStop schedule type.

The workflow trigger recurrence for ComputeStartStop schedule type.

The ContainerEndpoint.

The CreateMonitorAction.

The CronTrigger.

The CsvExportSummary.

The desired maximum forecast horizon in units of time-series frequency.

Custom inference server configurations.

Category:= CustomKeys AuthType:= CustomKeys (as type discriminator) Credentials:= {CustomKeys} as Microsoft.MachineLearning.AccountRP.Contracts.WorkspaceConnection.CustomKeys Target:= {any value} Use Metadata property bag for ApiVersion and other metadata fields

The CustomMetricThreshold.

The CustomMonitoringSignal.

N-Cross validations are specified by user.

The CustomSeasonality.

Specifies the custom service configuration.

The CustomTargetLags.

The CustomTargetRollingWindowSize.

The DatabaseSource.

The DataCollectionConfiguration.

The DataCollector.

The DataDriftMetricThresholdBase. Please note this is the abstract base class. The derived classes available for instantiation are: CategoricalDataDriftMetricThreshold and NumericalDataDriftMetricThreshold.

The DataDriftMonitoringSignal.

The DataImport.

The DataImportSource. Please note DataImportSource is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include DatabaseSource and FileSystemSource.

The DataQualityMetricThresholdBase. Please note this is the abstract base class. The derived classes available for instantiation are: CategoricalDataQualityMetricThreshold and NumericalDataQualityMetricThreshold.

The DataQualityMonitoringSignal.

DataReferenceCredential base class Please note this is the abstract base class. The derived classes available for instantiation are: AnonymousAccessCredential, DockerCredential, ManagedIdentityCredential, and SasCredential.

The DatasetExportSummary.

Dataset reference object.

Publishing destination registry asset information.

Credential for docker with username and password.

The DockerSetting.

The EmailMonitoringAlertNotificationSettings.

The EncryptionKeyVaultUpdateProperties.

The EncryptionUpdateProperties.

The EnvironmentVariable.

The ExportSummary. Please note ExportSummary is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include CsvExportSummary, CocoExportSummary and DatasetExportSummary.

The FeatureAttributionDriftMonitoringSignal.

The FeatureAttributionMetricThreshold.

The FeatureImportanceSettings.

Request payload for creating a backfill request for a given feature set version.

Response payload for creating a backfill request for a given feature set version.

The FeatureStoreSettings.

The FeatureSubset.

Specifies the feature window.

The FileSystemSource.

Fixed input data definition.

The desired maximum forecast horizon in units of time-series frequency. Please note this is the abstract base class. The derived classes available for instantiation are: AutoForecastHorizon and CustomForecastHorizon.

Forecasting seasonality. Please note this is the abstract base class. The derived classes available for instantiation are: AutoSeasonality and CustomSeasonality.

Forecasting specific parameters.

Forecasting Training related configuration.

FQDN Outbound Rule for the managed network of a machine learning workspace.

Generation safety quality metric threshold definition.

Generation safety quality monitoring signal definition.

Generation token statistics metric threshold definition.

Generation token statistics signal definition.

The GetBlobReferenceForConsumptionDto.

Defines a Sampling Algorithm that exhaustively generates every value combination in the space.

The HdfsDatastore.

Stops compute instance after user defined period of inactivity.

Image Classification. Multi-class image classification is used when an image is classified with only a single label from a set of classes - e.g. each image is classified as either an image of a 'cat' or a 'dog' or a 'duck'.

Image Classification Multilabel. Multi-label image classification is used when an image could have one or more labels from a set of labels - e.g. an image could be labeled with both 'cat' and 'dog'.

Image Instance Segmentation. Instance segmentation is used to identify objects in an image at the pixel level, drawing a polygon around each object in the image.

Limit settings for the AutoML job.

Returns metadata about the operating system image for this compute instance.

Distribution expressions to sweep over values of model settings. <example> Some examples are:

ModelName = "choice('seresnext', 'resnest50')";
LearningRate = "uniform(0.001, 0.01)";
LayersToFreeze = "choice(0, 2)";
```</example>
All distributions can be specified as distribution_name(min, max) or choice(val1, val2, ..., valn)
where distribution name can be: uniform, quniform, loguniform, etc
For more details on how to compose distribution expressions please check the documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
For more information on the available settings please visit the official documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

Distribution expressions to sweep over values of model settings. <example> Some examples are:

ModelName = "choice('seresnext', 'resnest50')";
LearningRate = "uniform(0.001, 0.01)";
LayersToFreeze = "choice(0, 2)";
```</example>
For more details on how to compose distribution expressions please check the documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
For more information on the available settings please visit the official documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

Distribution expressions to sweep over values of model settings. <example> Some examples are:

ModelName = "choice('seresnext', 'resnest50')";
LearningRate = "uniform(0.001, 0.01)";
LayersToFreeze = "choice(0, 2)";
```</example>
For more details on how to compose distribution expressions please check the documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
For more information on the available settings please visit the official documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

Settings used for training the model. For more information on the available settings please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

Settings used for training the model. For more information on the available settings please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

Settings used for training the model. For more information on the available settings please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

Image Object Detection. Object detection is used to identify objects in an image and locate each object with a bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.

The ImageSetting.

Model sweeping and hyperparameter sweeping related settings.

The ImportDataAction.

DTO object representing index column.

The InferencingServer. Please note InferencingServer is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include AzureMLBatchInferencingServer, AzureMLOnlineInferencingServer, CustomInferencingServer and TritonInferencingServer.

Intellectual Property details for a resource.

All nodes means the service will be running on all of the nodes of the job.

Abstract Nodes definition Please note this is the abstract base class. The derived classes available for instantiation are: JobAllNodes.

The JobQueueSettings.

Active message associated with project.

Jupyter kernel configuration.

The KerberosKeytabCredentials.

The KerberosKeytabSecrets.

The KerberosPasswordCredentials.

The KerberosPasswordSecrets.

Label category definition.

Label class definition.

Labeling data configuration definition.

Properties of a labeling job for image data.

Properties of a labeling job Please note LabelingJobMediaProperties is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include LabelingJobImageProperties and LabelingJobTextProperties.

Labeling job definition.

Properties of a labeling job for text data.

The LakeHouseArtifact.

Account key datastore credentials configuration.

Datastore account key secrets.

A Machine Learning compute based on AKS.

AKS properties.

Secrets related to a Machine Learning compute based on AKS.

Advance configuration for AKS networking.

The MachineLearningAllFeatures.

The MachineLearningAssetBase.

The MachineLearningAssetContainer.

Base definition for asset references. Please note this is the abstract base class. The derived classes available for instantiation are: MachineLearningIdAssetReference, MachineLearningDataPathAssetReference, and MachineLearningOutputPathAssetReference.

Labeling MLAssist configuration definition Please note MachineLearningAssistConfiguration is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include MLAssistConfigurationDisabled and MachineLearningAssistEnabledConfiguration.

Labeling MLAssist configuration definition when MLAssist is enabled.

Auto pause properties.

Auto scale properties.

Azure Blob datastore configuration.

Azure Data Lake Gen1 datastore configuration.

Azure Data Lake Gen2 datastore configuration.

Azure File datastore configuration.

Strictly used in update requests.

Batch inference settings per deployment.

Batch endpoint configuration.

Retry settings for a batch inference operation.

Configuration settings for Docker build context.

Certificate datastore credentials configuration.

Datastore certificate secrets.

Configuration for a scoring code asset.

Container for code asset versions.

Code asset version details.

Command job definition.

Command Job limit class.

Component container definition. https://docs.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-command

Definition of a component version: defines resources that span component types.

An Azure Machine Learning compute instance.

Defines an Aml Instance application and its connectivity endpoint URI.

A user that can be assigned to a compute instance.

Defines all connectivity endpoints and properties for an ComputeInstance.

Defines an Aml Instance container.

Describes information on user who created this ComputeInstance.

Defines an Aml Instance DataDisk.

Defines an Aml Instance DataMount.

Environment information.

The last operation on ComputeInstance.

Compute Instance properties.

Specifies policy and settings for SSH access.

AmlCompute update parameters.

Machine Learning compute object. Please note this is the abstract base class. The derived classes available for instantiation are: MachineLearningAksCompute, MachineLearningKubernetesCompute, AmlCompute, MachineLearningComputeInstance, MachineLearningVirtualMachineCompute, MachineLearningHDInsightCompute, MachineLearningDataFactoryCompute, MachineLearningDatabricksCompute, MachineLearningDataLakeAnalytics, and MachineLearningSynapseSpark.

The MachineLearningComputeRecurrenceSchedule.

Secrets related to a Machine Learning compute. Might differ for every type of compute. Please note this is the abstract base class. The derived classes available for instantiation are: MachineLearningAksComputeSecrets, MachineLearningVirtualMachineSecrets, and MachineLearningDatabricksComputeSecrets.

The schedule for automatically starting and stopping a compute instance.

A system service running on a compute.

The credentials for accessing a container registry.

Resource requirements for each container instance within an online deployment.

The MachineLearningContainerResourceSettings.

The MachineLearningCustomModelJobInput.

The MachineLearningCustomModelJobOutput.

A DataFactory compute.

Secrets related to a Machine Learning compute based on Databricks.

Properties of Databricks.

Container for data asset versions.

A DataFactory compute.

A DataLakeAnalytics compute.

Reference to an asset via its path in a datastore.

The options for the corresponding GetAll method.

Base definition for datastore credentials. Please note this is the abstract base class. The derived classes available for instantiation are: MachineLearningAccountKeyDatastoreCredentials, MachineLearningCertificateDatastoreCredentials, MachineLearningNoneDatastoreCredentials, MachineLearningSasDatastoreCredentials, and MachineLearningServicePrincipalDatastoreCredentials.

Base definition for datastore contents configuration. Please note this is the base class. The derived classes available for instantiation are: MachineLearningAzureBlobDatastore, MachineLearningAzureDataLakeGen1Datastore, MachineLearningAzureDataLakeGen2Datastore, MachineLearningAzureFileDatastore, and OneLakeDatastore.

Base definition for datastore secrets. Please note this is the abstract base class. The derived classes available for instantiation are: MachineLearningAccountKeyDatastoreSecrets, MachineLearningCertificateDatastoreSecrets, MachineLearningSasDatastoreSecrets, and MachineLearningServicePrincipalDatastoreSecrets.

The MachineLearningDataVersionCollectionGetAllOptions.

Data version base definition Please note this is the base class. The derived classes available for instantiation are: MachineLearningTable, MachineLearningUriFileDataVersion, and MachineLearningUriFolderDataVersion.

The MachineLearningDefaultScaleSettings.

The MachineLearningDeploymentLogs.

The MachineLearningDeploymentLogsContent.

The MachineLearningDeploymentResourceConfiguration.

Result of Diagnose.

The MachineLearningDiagnoseResultValue.

Base definition for job distribution configuration. Please note this is the abstract base class. The derived classes available for instantiation are: MpiDistributionConfiguration, PyTorchDistributionConfiguration, and TensorFlowDistributionConfiguration.

Early termination policies enable canceling poor-performing runs before they complete Please note this is the abstract base class. The derived classes available for instantiation are: BanditPolicy, MedianStoppingPolicy, and TruncationSelectionPolicy.

The KeyVault properties used for customer-managed-key encryption.

The MachineLearningEncryptionProperty.

The encryption settings of a workspace.

Keys for endpoint authentication.

Service Token.

Base definition for endpoint deployment.

The MachineLearningEndpointKeyRegenerateContent.

Inference Endpoint base definition.

The MachineLearningEndpointScheduleAction.

Container for environment specification versions.

Environment version details.

Error response.

The estimated price info for using a VM of a particular OS type, tier, etc.

The estimated price info for using a VM.

The options for the corresponding GetAll method.

DTO object representing feature.

The options for the corresponding GetAll method.

DTO object representing feature set.

Dto object representing the feature set job.

The options for the corresponding GetAll method.

DTO object representing feature set version.

The options for the corresponding GetAll method.

DTO object representing feature entity.

The options for the corresponding GetAll method.

DTO object representing feature entity version.

Featurization Configuration.

The MachineLearningFlavorData.

The MachineLearningFlowModelJobInput.

The MachineLearningFlowModelJobOutput.

Forecasting task in AutoML Table vertical.

The MachineLearningFqdnEndpoint.

The MachineLearningFqdnEndpointDetail.

The MachineLearningFqdnEndpoints.

The properties of a set of FQDN endpoints.

A HDInsight compute.

HDInsight compute properties.

Reference to an asset via its ARM resource ID.

Base definition for identity configuration. Please note this is the abstract base class. The derived classes available for instantiation are: AmlToken, MachineLearningManagedIdentity, and MachineLearningUserIdentity.

The MachineLearningInferenceContainerProperties.

The MachineLearningInferenceContainerRoute.

Instance type schema.

Resource requests/limits for this instance type.

The MachineLearningJobCollectionGetAllOptions.

Command job definition. Please note this is the abstract base class. The derived classes available for instantiation are: MachineLearningTableJobInput, MachineLearningCustomModelJobInput, MachineLearningFlowModelJobInput, MachineLearningLiteralJobInput, MachineLearningTritonModelJobInput, MachineLearningUriFileJobInput, and MachineLearningUriFolderJobInput.

The MachineLearningJobLimits. Please note this is the abstract base class. The derived classes available for instantiation are: MachineLearningCommandJobLimits and MachineLearningSweepJobLimits.

Job output definition container information on where to find job output/logs. Please note this is the abstract base class. The derived classes available for instantiation are: MachineLearningCustomModelJobOutput, MachineLearningFlowModelJobOutput, MachineLearningTableJobOutput, MachineLearningTritonModelJobOutput, MachineLearningUriFileJobOutput, and MachineLearningUriFolderJobOutput.

Azure Resource Manager resource envelope strictly used in update requests.

Base definition for a job. Please note this is the base class. The derived classes available for instantiation are: AutoMLJob, MachineLearningCommandJob, MachineLearningPipelineJob, SparkJob, and MachineLearningSweepJob.

The MachineLearningJobResourceConfiguration.

The MachineLearningJobScheduleAction.

Job endpoint definition.

Customer Key vault properties.

A Machine Learning compute based on Kubernetes Compute.

Properties specific to a KubernetesOnlineDeployment.

Kubernetes properties.

Literal input type.

Managed identity configuration.

The MachineLearningManagedIdentityAuthTypeWorkspaceConnection.

Properties specific to a ManagedOnlineDeployment.

The MachineLearningMarketplacePlan.

The MachineLearningMarketplaceSubscriptionProperties.

The MachineLearningModelContainerProperties.

The options for the corresponding GetAll method.

Model asset version details.

Counts of various compute node states on the amlCompute.

The MachineLearningNoneAuthTypeWorkspaceConnection.

Empty/none datastore credentials.

The MachineLearningNotebookPreparationError.

The MachineLearningNotebookResourceInfo.

Optimization objective.

Strictly used in update requests.

The MachineLearningOnlineDeploymentProperties. Please note this is the base class. The derived classes available for instantiation are: MachineLearningKubernetesOnlineDeployment and MachineLearningManagedOnlineDeployment.

The options for the corresponding GetAll method.

Online endpoint configuration.

Online deployment scoring requests configuration.

Online deployment scaling configuration. Please note this is the abstract base class. The derived classes available for instantiation are: MachineLearningDefaultScaleSettings and MachineLearningTargetUtilizationScaleSettings.

Outbound Rule for the managed network of a machine learning workspace. Please note this is the abstract base class. The derived classes available for instantiation are: FqdnOutboundRule, PrivateEndpointOutboundRule, and ServiceTagOutboundRule.

Reference to an asset via its path in a job output.

Managed service identity (system assigned and/or user assigned identities)

The MachineLearningPasswordDetail.

The MachineLearningPatAuthTypeWorkspaceConnection.

Pipeline Job definition: defines generic to MFE attributes.

The private endpoint resource of a workspace.

A private link resource.

A collection of information about the state of the connection between service consumer and provider.

Deployment container liveness/readiness probe configuration.

The properties for Quota update or retrieval.

Quota update parameters.

The MachineLearningRecurrenceSchedule.

The MachineLearningRecurrenceTrigger.

The options for the corresponding GetAll method.

Strictly used in update requests.

The MachineLearningResourceBase.

The MachineLearningResourceConfiguration.

The Resource Name.

Strictly used in update requests.

Strictly used in update requests.

The quota assigned to a resource.

The MachineLearningSasAuthTypeWorkspaceConnection.

SAS datastore credentials configuration.

Datastore SAS secrets.

The MachineLearningScheduleAction. Please note this is the abstract base class. The derived classes available for instantiation are: CreateMonitorAction, MachineLearningEndpointScheduleAction, and MachineLearningJobScheduleAction.

The MachineLearningScheduleBase.

Base definition of a schedule.

Script reference.

Customized setup scripts.

Strictly used in update requests.

Service Principal datastore credentials configuration.

Datastore Service Principal secrets.

The MachineLearningSharedPrivateLinkResource.

The resource model definition representing SKU.

SKU capacity information.

Fulfills ARM Contract requirement to list all available SKUS for a resource.

Common SKU definition.

SkuSetting fulfills the need for stripped down SKU info in ARM contract.

The ssl configuration for scoring.

Advances setting to customize StackEnsemble run.

Sweep job definition.

Sweep Job limit class.

A SynapseSpark compute.

The MachineLearningSynapseSparkProperties.

MLTable data definition.

The MachineLearningTableJobInput.

The MachineLearningTableJobOutput.

The MachineLearningTargetUtilizationScaleSettings.

Training related configuration.

Trial component definition.

The MachineLearningTriggerBase. Please note this is the abstract base class. The derived classes available for instantiation are: MachineLearningRecurrenceTrigger and CronTrigger.

The MachineLearningTritonModelJobInput.

The MachineLearningTritonModelJobOutput.

uri-file data version entity.

The MachineLearningUriFileJobInput.

The MachineLearningUriFileJobOutput.

uri-folder data version entity.

The MachineLearningUriFolderJobInput.

The MachineLearningUriFolderJobOutput.

Describes AML Resource Usage.

The Usage Names.

Settings for user account that gets created on each on the nodes of a compute.

Features enabled for a workspace.

User identity configuration.

The MachineLearningUsernamePasswordAuthTypeWorkspaceConnection.

A Machine Learning compute based on Azure Virtual Machines.

The MachineLearningVirtualMachineProperties.

Secrets related to a Machine Learning compute based on AKS.

Describes the properties of a VM size.

Admin credentials for virtual machine.

Webhook base Please note this is the abstract base class. The derived classes available for instantiation are: AzureDevOpsWebhook.

The MachineLearningWorkspaceConnectionManagedIdentity.

The properties that the machine learning workspace connection will be updated with.

The MachineLearningWorkspaceConnectionProperties. Please note MachineLearningWorkspaceConnectionProperties is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include MachineLearningManagedIdentityAuthTypeWorkspaceConnection, MachineLearningNoneAuthTypeWorkspaceConnection, MachineLearningPatAuthTypeWorkspaceConnection, MachineLearningSasAuthTypeWorkspaceConnection and MachineLearningUsernamePasswordAuthTypeWorkspaceConnection.

The MachineLearningWorkspaceConnectionUsernamePassword.

Parameters to diagnose a workspace.

The MachineLearningWorkspaceDiagnoseProperties.

The MachineLearningWorkspaceDiagnoseResult.

The MachineLearningWorkspaceGetKeysResult.

The MachineLearningWorkspaceGetNotebookKeysResult.

The MachineLearningWorkspaceGetStorageAccountKeysResult.

The MachineLearningWorkspaceNotebookAccessTokenResult.

The parameters for updating a machine learning workspace.

The parameters for updating a machine learning workspace.

The result of update workspace quota.

The properties for update Quota response.

Managed compute identity definition.

Credential for user managed identity.

Managed Network Provisioning options for managed network of a machine learning workspace.

Status of the Provisioning for the managed network of a machine learning workspace.

Managed Network settings for a machine learning workspace.

Details for managed resource group assigned identities.

The MaterializationSettings.

Defines an early termination policy based on running averages of the primary metric of all runs.

Labeling MLAssist configuration definition when MLAssist is disabled.

Model configuration options.

Model package operation request properties.

Model package input options.

Package response returned after async package operation completes successfully.

The ModelPerformanceMetricThresholdBase. Please note ModelPerformanceMetricThresholdBase is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include ClassificationModelPerformanceMetricThreshold and RegressionModelPerformanceMetricThreshold.

Model performance signal definition.

Monitor compute configuration base definition. Please note this is the abstract base class. The derived classes available for instantiation are: MonitorServerlessSparkCompute.

Monitor compute identity base definition. Please note this is the abstract base class. The derived classes available for instantiation are: AmlTokenComputeIdentity and ManagedComputeIdentity.

The MonitorDefinition.

The MonitoringAlertNotificationSettingsBase. Please note MonitoringAlertNotificationSettingsBase is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include AzMonMonitoringAlertNotificationSettings and EmailMonitoringAlertNotificationSettings.

The MonitoringDataSegment.

The MonitoringFeatureFilterBase. Please note this is the abstract base class. The derived classes available for instantiation are: MachineLearningAllFeatures, FeatureSubset, and TopNFeaturesByAttribution.

Monitoring input data base definition. Please note this is the abstract base class. The derived classes available for instantiation are: FixedInputData, RollingInputData, and StaticInputData.

The MonitoringSignalBase. Please note this is the abstract base class. The derived classes available for instantiation are: CustomMonitoringSignal, DataDriftMonitoringSignal, DataQualityMonitoringSignal, FeatureAttributionDriftMonitoringSignal, and PredictionDriftMonitoringSignal.

Monitoring target definition.

Monitoring workspace connection definition.

Monitor serverless spark compute definition.

The MountBindOptions.

MPI distribution configuration.

N-Cross validations value. Please note this is the abstract base class. The derived classes available for instantiation are: AutoNCrossValidations and CustomNCrossValidations.

Fixed training parameters that won't be swept over during AutoML NLP training.

Stringified search spaces for each parameter. See below examples.

Model sweeping and hyperparameter tuning related settings.

Job execution constraints.

Configuration for notification.

The NumericalDataDriftMetricThreshold.

The NumericalDataQualityMetricThreshold.

The NumericalPredictionDriftMetricThreshold.

The OAuth2AuthTypeWorkspaceConnectionProperties.

OneLake artifact (data source) configuration. Please note this is the abstract base class. The derived classes available for instantiation are: LakeHouseArtifact.

OneLake (Trident) datastore configuration.

Online inference configuration options.

Returns metadata about the os patching.

The PackageInputPathBase. Please note PackageInputPathBase is the base class. According to the scenario, a derived class of the base class might need to be assigned here, or this property needs to be casted to one of the possible derived classes. The available derived classes include PackageInputPathId, PackageInputPathVersion and PackageInputPathUri.

Package input path specified with a resource id.

Package input path specified as an url.

Package input path specified with name and version.

The PendingUploadCredentialDto. Please note this is the abstract base class. The derived classes available for instantiation are: SasCredentialDto.

The PendingUploadRequestDto.

The PendingUploadResponseDto.

The PredictionDriftMetricThresholdBase. Please note this is the abstract base class. The derived classes available for instantiation are: CategoricalPredictionDriftMetricThreshold and NumericalPredictionDriftMetricThreshold.

The PredictionDriftMonitoringSignal.

The private endpoint resource.

Private Endpoint destination for a Private Endpoint Outbound Rule for the managed network of a machine learning workspace.

Private Endpoint Outbound Rule for the managed network of a machine learning workspace.

Progress metrics definition.

PyTorch distribution configuration.

Defines a Sampling Algorithm that generates values randomly.

Ray distribution configuration.

Details of ACR account to be used for the Registry.

The PE network resource that is linked to this PE connection.

Private endpoint connection definition.

The connection state.

Details for each region the registry is in.

The RegressionModelPerformanceMetricThreshold.

Regression Training related configuration.

Rolling input data definition.

The Sampling Algorithm used to generate hyperparameter values, along with properties to configure the algorithm Please note this is the abstract base class. The derived classes available for instantiation are: BayesianSamplingAlgorithm, GridSamplingAlgorithm, and RandomSamplingAlgorithm.

Access with full SAS uri.

The SasCredentialDto.

Secret Configuration definition.

Secret expiration configuration.

The ServerlessComputeSettings.

The ServerlessEndpointProperties.

The ServerlessInferenceEndpoint.

The ServicePrincipalAuthTypeWorkspaceConnectionProperties.

Service Tag destination for a Service Tag Outbound Rule for the managed network of a machine learning workspace.

Service Tag Outbound Rule for the managed network of a machine learning workspace.

Spark job definition.

Spark job entry point definition. Please note this is the abstract base class. The derived classes available for instantiation are: SparkJobPythonEntry and SparkJobScalaEntry.

The SparkJobPythonEntry.

The SparkJobScalaEntry.

The SparkResourceConfiguration.

Static input data definition.

Details of storage account to be used for the Registry.

The SystemCreatedAcrAccount.

The SystemCreatedStorageAccount.

Fixed training parameters that won't be swept over during AutoML Table training.

The TableParameterSubspace.

The TableSweepSettings.

Featurization Configuration.

Job execution constraints.

The number of past periods to lag from the target column. Please note this is the abstract base class. The derived classes available for instantiation are: AutoTargetLags and CustomTargetLags.

Forecasting target rolling window size. Please note this is the abstract base class. The derived classes available for instantiation are: AutoTargetRollingWindowSize and CustomTargetRollingWindowSize.

TensorFlow distribution configuration.

Text Classification task in AutoML NLP vertical. NLP - Natural Language Processing.

Text Classification Multilabel task in AutoML NLP vertical. NLP - Natural Language Processing.

Text-NER task in AutoML NLP vertical. NER - Named Entity Recognition. NLP - Natural Language Processing.

The TopNFeaturesByAttribution.

Trailing input data definition.

Triton inferencing server configurations.

Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.

The VolumeDefinition.

The WorkspaceConnectionAccessKey.

ClientId and ClientSecret are required. Other properties are optional depending on each OAuth2 provider's implementation.

The WorkspaceConnectionServicePrincipal.

WorkspaceHub's configuration object.

The AutoDeleteCondition.

Enum for all Regression models supported by AutoML.

Primary metrics for Regression task.

AutoRebuild setting for the derived image.

Enum for all classification models supported by AutoML.

Provisioning state of capability host.

Enum for all classification models supported by AutoML.

The ClassificationModelPerformanceMetric.

Primary metrics for classification multilabel tasks.

Primary metrics for classification tasks.

Protocol over which communication will happen over this endpoint.

Specifies the status of content safety.

Enum to determine the datastore contents type.

Enum to determine the email notification type.

Connection status of the service consumer with the service provider.

Type of the Environment Variable. Possible values are: local - For local variable.

The mode of operation for computing feature importance.

The FeatureStoreJobType.

Firewall Sku used for FQDN Rules.

Enum for all forecasting models supported by AutoML.

Primary metrics for Forecasting task.

Generation safety quality metric enum.

Generation token statistics metric enum.

Annotation type of image data.

Type of the image. Possible values are: docker - For docker images. azureml - For AzureML Environment images (custom and curated).

Whether IncrementalDataRefresh is enabled.

Primary metrics for InstanceSegmentation tasks.

Protection level associated with the Intellectual Property.

Isolation mode for the managed network of a machine learning workspace.

Enum to determine the Job Input Type.

Enum to determine the job provisioning state.

The JobStatusMessageLevel.

Enum to determine the job tier.

Enum to determine the type of job.

Whether multiSelect is enabled.

Learning rate scheduler enum.

The LogTrainingMetric.

The LogValidationLoss.

Allocation state of the compute. Possible values are: steady - Indicates that the compute is not resizing. There are no changes to the number of compute nodes in the compute in progress. A compute enters this state when it is created and when no operations are being performed on the compute to change the number of compute nodes. resizing - Indicates that the compute is resizing; that is, compute nodes are being added to or removed from the compute.

Policy for sharing applications on this compute instance among users of parent workspace. If Personal, only the creator can access applications on this compute instance. When Shared, any workspace user can access applications on this instance depending on his/her assigned role.

Log verbosity for batch inferencing. Increasing verbosity order for logging is : Warning, Info and Debug. The default value is Info.

Enum to determine how batch inferencing will handle output.

Three lettered code specifying the currency of the VM price. Example: USD.

Caching type of Data Disk.

Intended usage of the cluster.

The Compute Instance Authorization type. Available values are personal (default).

Auto save settings.

Current state of an ComputeInstance.

[Required] The compute power action.

The current deployment state of schedule.

Enum to describe the frequency of a compute recurrence schedule.

Enum of weekday.

Category of the connection.

Enum to determine the type of data.

Enum of weekday.

Possible values for DeploymentProvisioningState.

Level of workspace setup error.

Enum to determine whether PublicNetworkAccess is Enabled or Disabled for egress of a deployment.

Indicates whether or not the encryption is enabled for the workspace.

Enum to determine endpoint authentication mode.

Enum to determine endpoint compute type.

State of endpoint provisioning.

Environment type is either user created or curated by Azure ML service.

Flag for generating lags for the numeric features.

Featurization mode - determines data featurization mode.

Indicates whether mlflow autologger is enabled for notebooks.

Enum to determine the state of mlflow autologger.

Defines supported metric goals for hyperparameter tuning.

Enum to determine the input data delivery mode.

The status of a job.

Load Balancer Type.

Enum for setting log verbosity.

Image model size.

Mount Action.

Mount state.

network of this container.

State of the compute node. Values are idle, running, preparing, unusable, leaving and preempted.

The type of operating system.

Name of the last operation.

Operation status.

Trigger of operation.

Compute OS Type.

Output data delivery mode enums.

The current provisioning state.

The private endpoint connection status.

The provision state of the cluster. Valid values are Unknown, Updating, Provisioning, Succeeded, and Failed.

Whether requests from Public Network are allowed.

Whether requests from the public network are allowed.

An enum describing the unit of quota measurement.

Enum to describe the frequency of a recurrence schedule.

State of the public SSH port. Possible values are: Disabled - Indicates that the public ssh port is closed on all nodes of the cluster. Enabled - Indicates that the public ssh port is open on all nodes of the cluster. NotSpecified - Indicates that the public ssh port is closed on all nodes of the cluster if VNet is defined, else is open all public nodes. It can be default only during cluster creation time, after creation it will be either enabled or disabled.

The current deployment state of schedule.

Is the schedule enabled or disabled?.

The parameter defining how if AutoML should handle short time series.

Node scaling setting for the compute sku.

Data source type.

State of the public SSH port. Possible values are: Disabled - Indicates that the public ssh port is closed on this instance. Enabled - Indicates that the public ssh port is open and accessible according to the VNet/subnet policy if applicable.

Enable or disable ssl for scoring.

The meta-learner is a model trained on the output of the individual heterogeneous models. Default meta-learners are LogisticRegression for classification tasks (or LogisticRegressionCV if cross-validation is enabled) and ElasticNet for regression/forecasting tasks (or ElasticNetCV if cross-validation is enabled). This parameter can be one of the following strings: LogisticRegression, LogisticRegressionCV, LightGBMClassifier, ElasticNet, ElasticNetCV, LightGBMRegressor, or LinearRegression

type of this storage account.

The unit of time measurement for the specified VM price. Example: OneHour.

An enum describing the unit of usage measurement.

Configure STL Decomposition of the time-series target column.

Operating system type used by the VM.

Virtual Machine priority.

The type of the VM.

Status of update workspace quota.

The Kind of the managed network. Users can switch from V1 to V2 for granular access controls, but cannot switch back to V1 once V2 is enabled.

Status for the managed network of a machine learning workspace.

Model task type enum.

The MonitoringModelType.

The MonitoringNotificationMode.

Mount Mode.

The action enum for networking rule.

Enum of learning rate schedulers that aligns with those supported by HF.

Primary metrics for Image ObjectDetection task.

Category of a managed network Outbound Rule of a machine learning workspace.

Type of a managed network Outbound Rule of a machine learning workspace.

Package build state returned in package response.

Mounting type of the model or the inputs.

Type of the inputs.

The os patching status.

Type of storage to use for the pending upload location.

Enum to determine whether PublicNetworkAccess is Enabled or Disabled.

The specific type of random algorithm.

Provisioning state of registry asset.

The RegressionModelPerformanceMetric.

State of the Serverless Endpoint.

Stochastic optimizer for image models.

The auth mode used for accessing the system datastores of the workspace.

Target aggregate function.

Annotation type of text data.

Training mode dictates whether to use distributed training or not.

Metric computation method to use for validation metrics in image tasks.

Type of Volume Definition. Possible Values: bind,volume,tmpfs,npipe.

Group based on connection category.

This field is required to be implemented by the Resource Provider if the service has more than one tier, but is not required on a PUT.