Microsoft.MachineLearningServices workspaces/connections 2020-09-01-preview
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Bicep resource definition
The workspaces/connections resource type can be deployed with operations that target:
- Resource groups - See resource group deployment commands
For a list of changed properties in each API version, see change log.
Resource format
To create a Microsoft.MachineLearningServices/workspaces/connections resource, add the following Bicep to your template.
resource symbolicname 'Microsoft.MachineLearningServices/workspaces/connections@2020-09-01-preview' = {
parent: resourceSymbolicName
name: 'string'
properties: {
authType: 'string'
category: 'string'
target: 'string'
value: 'string'
}
}
Property values
Microsoft.MachineLearningServices/workspaces/connections
| Name | Description | Value |
|---|---|---|
| name | The resource name | string (required) |
| parent | In Bicep, you can specify the parent resource for a child resource. You only need to add this property when the child resource is declared outside of the parent resource. For more information, see Child resource outside parent resource. |
Symbolic name for resource of type: workspaces |
| properties | Properties of workspace connection. | WorkspaceConnectionProps |
WorkspaceConnectionProps
| Name | Description | Value |
|---|---|---|
| authType | Authorization type of the workspace connection. | string |
| category | Category of the workspace connection. | string |
| target | Target of the workspace connection. | string |
| value | Value details of the workspace connection. | string |
Quickstart samples
The following quickstart samples deploy this resource type.
| Bicep File | Description |
|---|---|
| Azure AI Studio basic setup | This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio basic setup | This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio basic setup | This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio Network Restricted | This set of templates demonstrates how to set up Azure AI Studio with private link and egress disabled, using Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio Network Restricted | This set of templates demonstrates how to set up Azure AI Studio with private link and egress disabled, using Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio with Microsoft Entra ID Authentication | This set of templates demonstrates how to set up Azure AI Studio with Microsoft Entra ID authentication for dependent resources, such as Azure AI Services and Azure Storage. |
| Deploy Secure Azure AI Studio with a managed virtual network | This template creates a secure Azure AI Studio environment with robust network and identity security restrictions. |
ARM template resource definition
The workspaces/connections resource type can be deployed with operations that target:
- Resource groups - See resource group deployment commands
For a list of changed properties in each API version, see change log.
Resource format
To create a Microsoft.MachineLearningServices/workspaces/connections resource, add the following JSON to your template.
{
"type": "Microsoft.MachineLearningServices/workspaces/connections",
"apiVersion": "2020-09-01-preview",
"name": "string",
"properties": {
"authType": "string",
"category": "string",
"target": "string",
"value": "string"
}
}
Property values
Microsoft.MachineLearningServices/workspaces/connections
| Name | Description | Value |
|---|---|---|
| apiVersion | The api version | '2020-09-01-preview' |
| name | The resource name | string (required) |
| properties | Properties of workspace connection. | WorkspaceConnectionProps |
| type | The resource type | 'Microsoft.MachineLearningServices/workspaces/connections' |
WorkspaceConnectionProps
| Name | Description | Value |
|---|---|---|
| authType | Authorization type of the workspace connection. | string |
| category | Category of the workspace connection. | string |
| target | Target of the workspace connection. | string |
| value | Value details of the workspace connection. | string |
Quickstart templates
The following quickstart templates deploy this resource type.
| Template | Description |
|---|---|
Azure AI Studio basic setup |
This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio basic setup |
This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio basic setup |
This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio Network Restricted |
This set of templates demonstrates how to set up Azure AI Studio with private link and egress disabled, using Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio Network Restricted |
This set of templates demonstrates how to set up Azure AI Studio with private link and egress disabled, using Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio with Microsoft Entra ID Authentication |
This set of templates demonstrates how to set up Azure AI Studio with Microsoft Entra ID authentication for dependent resources, such as Azure AI Services and Azure Storage. |
| Deploy Secure Azure AI Studio with a managed virtual network |
This template creates a secure Azure AI Studio environment with robust network and identity security restrictions. |
Terraform (AzAPI provider) resource definition
The workspaces/connections resource type can be deployed with operations that target:
- Resource groups
For a list of changed properties in each API version, see change log.
Resource format
To create a Microsoft.MachineLearningServices/workspaces/connections resource, add the following Terraform to your template.
resource "azapi_resource" "symbolicname" {
type = "Microsoft.MachineLearningServices/workspaces/connections@2020-09-01-preview"
name = "string"
body = jsonencode({
properties = {
authType = "string"
category = "string"
target = "string"
value = "string"
}
})
}
Property values
Microsoft.MachineLearningServices/workspaces/connections
| Name | Description | Value |
|---|---|---|
| name | The resource name | string (required) |
| parent_id | The ID of the resource that is the parent for this resource. | ID for resource of type: workspaces |
| properties | Properties of workspace connection. | WorkspaceConnectionProps |
| type | The resource type | "Microsoft.MachineLearningServices/workspaces/connections@2020-09-01-preview" |
WorkspaceConnectionProps
| Name | Description | Value |
|---|---|---|
| authType | Authorization type of the workspace connection. | string |
| category | Category of the workspace connection. | string |
| target | Target of the workspace connection. | string |
| value | Value details of the workspace connection. | string |