Package-level declarations
Types
Level at which content is filtered.
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.
Defines if image needs to be rebuilt based on base image changes.
Logging level for batch inference operation.
Indicates how the output will be organized.
Enum for all classification models supported by AutoML.
Kind of this capability host.
Required The categorical data drift metric to calculate.
Required The categorical data quality metric to calculate.
Required The categorical prediction drift metric to calculate.
Enum for all classification models supported by AutoML.
Primary metric to optimize for this task.
Primary metric for Text-Classification task.
Intended usage of the cluster
The Compute Instance Authorization type. Available values are personal (default).
Required The compute power action.
Required The frequency to trigger schedule.
Required The schedule trigger type.
Enum of weekday
Category of the connection
The type of container to retrieve logs from.
Required Specifies the status of content safety.
Enable or disable data collection.
Specifies dataset type.
Specifies datastore type.
Deployment model version upgrade option.
If Enabled, allow egress public network access. If Disabled, this will create secure egress. Default: Enabled.
Enum to determine the email notification type.
Indicates whether or not the encryption is enabled for the workspace.
Required The authentication method for invoking the endpoint (data plane operation). Use 'Key' for key-based authentication. Use 'AMLToken' for Azure Machine Learning token-based authentication. Use 'AADToken' for Microsoft Entra token-based authentication.
Required The compute type of the endpoint.
Connection status of the service consumer with the service provider
Type of the Environment Variable. Possible values are: local - For local variable
Required The feature attribution metric to calculate.
Specifies the data type
The mode of operation for computing feature importance.
Flag for generating lags for the numeric features with 'auto' or null.
Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
Firewall Sku used for FQDN Rules
Enum for all forecasting models supported by AutoML.
Primary metric for forecasting task.
Annotation type of image labeling job.
Indicates whether to enable incremental data refresh.
Input Asset Delivery Mode.
Primary metric to optimize for this task.
Isolation mode for the managed network of a machine learning workspace.
Required Specifies the type of job.
Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
Type of the link target.
Load Balancer Type
Log verbosity for the job.
Status for the managed network of a machine learning workspace.
Type of managed service identity (where both SystemAssigned and UserAssigned types are allowed).
Specifies the stores to which materialization should happen
Required The machine learning task type of the monitored model.
Required Specifies the data type of the metric threshold.
Indicates whether it is allowed to select multiple classes in this category.
Required The numerical data drift metric to calculate.
Required The numerical data quality metric to calculate.
Required The numerical prediction drift metric to calculate.
Primary metric to optimize for this task.
The OS type of the environment.
Output Asset Delivery Mode.
Indicates whether the connection has been Approved/Rejected/Removed by the owner of the service.
Whether requests from Public Network are allowed.
Set to "Enabled" for endpoints that should allow public access when Private Link is enabled.
Content source to apply the Content Filters.
Content Filters mode.
Content Filters policy type.
The specific type of random algorithm
Required The frequency to trigger schedule.
Enum for all Regression models supported by AutoML.
Primary metric for regression task.
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 identity type.
When model data is collected to blob storage, we need to roll the data to different path to avoid logging all of them in a single blob file. If the rolling rate is hour, all data will be collected in the blob path /yyyy/MM/dd/HH/. If it's day, all data will be collected in blob path /yyyy/MM/dd/. The other benefit of rolling path is that model monitoring ui is able to select a time range of data very quickly.
The action enum for networking rule.
Category of a managed network Outbound Rule of a machine learning workspace.
Type of a managed network Outbound Rule of a machine learning workspace.
Required The algorithm used for generating hyperparameter values, along with configuration properties
The current deployment state of schedule.
Is the schedule enabled or disabled?
Required Specifies the authentication mode for the Serverless endpoint.
Indicates which identity to use to authenticate service data access to customer's storage.
The parameter defining how if AutoML should handle short time series.
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.
Type of optimizer.
The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".
Annotation type of text labeling job.
Metric computation method to use for validation metrics.
format for the workspace connection value
Virtual Machine priority
Type of Volume Definition. Possible Values: bind,volume,tmpfs,npipe