# Generated by API Evangelist (build-phrasing.py). Our phrasing, not observed demand.
overlay: 1.0.0
info:
  title: API Evangelist conversational phrasing for Microsoft Azure Azure Machine Learning Services Managed…
  version: 1.0.0
extends: openapi/microsoft-azure-managednetwork-api-openapi.yml
actions:
- target: $.info
  update:
    x-apievangelist-phrasing:
      method: generated
      generated: '2026-09-26'
      generator: build-phrasing.py
      label: Generated by API Evangelist
      operations: 5
- target: $.paths['/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/outboundRules'].get
  update:
    x-apievangelist-phrasing:
      intent: List outbound rules of an ML workspace network
      effect: read
      questions:
      - Which outbound rules are configured on my machine learning workspace's managed network?
      - Can I see what egress my Azure ML workspace is allowed?
      instructions:
      - text: List the managed network outbound rules for ML workspace {workspace}.
        slots:
          workspace: path.workspaceName
      - text: Show all egress rules on {workspace}.
        slots:
          workspace: path.workspaceName
      method: generated
      generated: '2026-09-26'
- target: $.paths['/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/outboundRules/{ruleName}'].get
  update:
    x-apievangelist-phrasing:
      intent: Get an outbound rule of an ML workspace
      effect: read
      questions:
      - How do I inspect one outbound rule on my ML workspace's managed network?
      - What destination does a specific workspace outbound rule allow?
      instructions:
      - text: Get outbound rule {rule} from ML workspace {workspace}.
        slots:
          rule: path.ruleName
          workspace: path.workspaceName
      - text: Show the definition of egress rule {rule}.
        slots:
          rule: path.ruleName
      method: generated
      generated: '2026-09-26'
- target: $.paths['/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/outboundRules/{ruleName}'].put
  update:
    x-apievangelist-phrasing:
      intent: Create or update an ML workspace outbound rule
      effect: write
      questions:
      - How do I allow my Azure ML workspace to reach an outside endpoint?
      - Can I add an outbound rule to a machine learning workspace's managed network?
      instructions:
      - text: Create outbound rule {rule} on ML workspace {workspace} with {properties}.
        slots:
          rule: path.ruleName
          workspace: path.workspaceName
          properties: requestBody.properties
      - text: Add egress rule {rule} to the managed network of {workspace}.
        slots:
          rule: path.ruleName
          workspace: path.workspaceName
      method: generated
      generated: '2026-09-26'
- target: $.paths['/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/outboundRules/{ruleName}'].delete
  update:
    x-apievangelist-phrasing:
      intent: Delete an ML workspace outbound rule
      effect: destructive
      questions:
      - How do I revoke an outbound rule from my ML workspace?
      - Can I delete one egress rule from a workspace's managed network?
      instructions:
      - text: Delete outbound rule {rule} from ML workspace {workspace}.
        slots:
          rule: path.ruleName
          workspace: path.workspaceName
      - text: Remove egress rule {rule} from {workspace}.
        slots:
          rule: path.ruleName
          workspace: path.workspaceName
      method: generated
      generated: '2026-09-26'
- target: $.paths['/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/provisionManagedNetwork'].post
  update:
    x-apievangelist-phrasing:
      intent: Provision the managed network of an ML workspace
      effect: write
      questions:
      - How do I provision the managed virtual network for my machine learning workspace?
      - Can the managed network provisioning include Spark support?
      instructions:
      - text: Provision the managed network for ML workspace {workspace}.
        slots:
          workspace: path.workspaceName
      - text: Provision the managed network of {workspace} with includeSpark {include_spark}.
        slots:
          workspace: path.workspaceName
          include_spark: requestBody.includeSpark
      method: generated
      generated: '2026-09-26'