steampipe plugin install azuresteampipe plugin install azure

Table: azure_machine_learning_workspace - Query Azure Machine Learning Workspaces using SQL

Azure Machine Learning is a cloud-based environment that enables developers to build, train, and deploy machine learning models. Workspaces in Azure Machine Learning are the top-level resource for the service, providing a centralized place to work with all the artifacts you create. A workspace is tied to an Azure subscription and the resources are used to run the workspace and its experiments.

Table Usage Guide

The azure_machine_learning_workspace table provides insights into Azure Machine Learning Workspaces. As a data scientist or machine learning engineer, you can explore workspace-specific details through this table, including configurations, status, and properties. Utilize it to uncover information about workspace, such as its associated resources, location, and SKU details, enabling effective management and optimization of your machine learning experiments.

Examples

Basic info

Explore which Azure Machine Learning Workspaces are currently provisioned and understand their types. This can be useful for managing resources and understanding the distribution of workspace types within your Azure environment.

select
  name,
  id,
  type,
  provisioning_state
from
  azure_machine_learning_workspace;

select
  name,
  id,
  type,
  provisioning_state
from
  azure_machine_learning_workspace;

List system assigned identity type workspace

Determine the areas in which system-assigned identities are used within Azure's machine learning workspace. This can help streamline system management by identifying where these automated identities are deployed.

select
  name,
  id,
  type,
  identity ->> 'type' as identity_type
from
  azure_machine_learning_workspace
where
  identity ->> 'type' = 'SystemAssigned';

select
  name,
  id,
  type,
  json_extract(identity, '$.type') as identity_type
from
  azure_machine_learning_workspace
where
  json_extract(identity, '$.type') = 'SystemAssigned';

List key vault used by workspaces with soft deletion disabled

Determine the areas in which the key vault used by workspaces has soft deletion disabled. This is beneficial in identifying potential vulnerabilities and ensuring data protection and recovery strategies are in place.

select
  m.name as workspace_name,
  m.id as workspace_id,
  v.soft_delete_enabled
from
  azure_machine_learning_workspace as m,
  azure_key_vault as v
where
  lower(m.key_vault) = lower(v.id)
  and not v.soft_delete_enabled;

select
  m.name as workspace_name,
  m.id as workspace_id,
  v.soft_delete_enabled
from
  azure_machine_learning_workspace as m,
  azure_key_vault as v
where
  lower(m.key_vault) = lower(v.id)
  and not v.soft_delete_enabled;

Control examples

Azure Machine Learning workspaces should be encrypted with a customer-managed key

Schema for azure_machine_learning_workspace

Name	Type	Operators	Description
_ctx	jsonb		Steampipe context in JSON form, e.g. connection_name.
akas	jsonb		Array of globally unique identifier strings (also known as) for the resource.
application_insights	text		ARM id of the application insights associated with this workspace. This cannot be changed once the workspace has been created.
cloud_environment	text		The Azure Cloud Environment.
container_registry	text		ARM id of the container registry associated with this workspace. This cannot be changed once the workspace has been created.
creation_time	timestamp with time zone		The creation time for this workspace resource.
description	text		The description of this workspace.
diagnostic_settings	jsonb		A list of active diagnostic settings for the azure ML workspace.
discovery_url	text		ARM id of the container registry associated with this workspace. This cannot be changed once the workspace has been created.
encryption	jsonb		The encryption settings of Azure ML workspace.
friendly_name	text		The friendly name for this workspace. This name in mutable.
hbi_workspace	boolean		The flag to signal HBI data in the workspace and reduce diagnostic data collected by the service.
id	text		The resource identifier.
identity	jsonb		The identity of the resource.
key_vault	text		ARM id of the key vault associated with this workspace, This cannot be changed once the workspace has been created.
location	text		The location of the resource. This cannot be changed after the resource is created.
name	text	=	The resource name.
provisioning_state	text		The current deployment state of workspace resource, The provisioningState is to indicate states for resource provisioning. Possible values include: 'Unknown', 'Updating', 'Creating', 'Deleting', 'Succeeded', 'Failed', 'Canceled'.
region	text		The Azure region/location in which the resource is located.
resource_group	text	=	The resource group which holds this resource.
service_provisioned_resource_group	text		The name of the managed resource group created by workspace RP in customer subscription if the workspace is CMK workspace.
sku_name	text		Name of the sku.
sku_tier	text		Tier of the sku like Basic or Enterprise.
storage_account	text		ARM id of the storage account associated with this workspace. This cannot be changed once the workspace has been created.
subscription_id	text		The Azure Subscription ID in which the resource is located.
tags	jsonb		A map of tags for the resource.
title	text		Title of the resource.
type	text		The resource type.
workspace_id	text		The immutable id associated with this workspace.

Export

This table is available as a standalone Exporter CLI. Steampipe exporters are stand-alone binaries that allow you to extract data using Steampipe plugins without a database.

You can download the tarball for your platform from the Releases page, but it is simplest to install them with the steampipe_export_installer.sh script:

/bin/sh -c "$(curl -fsSL https://steampipe.io/install/export.sh)" -- azure

You can pass the configuration to the command with the --config argument:

steampipe_export_azure --config '<your_config>' azure_machine_learning_workspace