Check OS Image Version

01 Sign in to the Microsoft Azure Portal.

02 Navigate to All resources blade available at https://portal.azure.com/#browse/all to access all your Microsoft Azure cloud resources.

03 Choose the Azure subscription that you want to access from the Subscription equalls all filter box and choose Apply.

04 From the Type equals all filter box, select Type for Filter, Equals for Operator, and Azure Machine Learning workspace for Value, then choose Apply to list the Azure Machine Learning workspaces available in the selected subscription.

05 Click on the name (link) of the Machine Learning workspace that you want to access.

06 In the resource navigation panel, select Overview, and choose Launch studio to open the Azure Machine Learning Studio.

07 In the left navigation panel, under Manage, choose Compute, and select the Compute instances tab.

08 In Azure Machine Learning, compute instances receive the latest VM images at the time of provisioning. However, once a compute instance is deployed, it does not get actively updated, even though Microsoft releases new VM images on a monthly basis. While you can query an instance's operating system version, to stay up to date with the latest software updates and security patches, you must re-create your Machine Learning compute instances to get the latest OS image. Choose New and perform the following actions to create your new compute instance:

1. For Required settings, enter a name for the new instance in the Compute name box, select the instance type from the Virtual machine type, choose Select from all options under Virtual machine size, and select the appropriate VM size for your instance. Choose Next to continue the setup process.
2. (Optional) For Scheduling, choose whether to schedule the compute to start or stop on a recurring basis. You can also configure instance auto shutdown on this step. Choose Next to continue.
3. For Security, configure security settings such as SSH access, virtual network injection, root access, Single Sign-On (SSO), and managed identity for your new compute instance. Choose Next to continue the setup.
4. (Optional) For Applications, choose Add application, and add custom applications you may want to use on your compute instance. You can also choose to provision the new instance with a creation and/or startup script on this step. Choose Next to continue.
5. (Optional) For Tags, use the Name and Value text fields to create the tagging schema for your compute instance. Choose Next to continue the setup.
6. For Review, review the instance configuration and choose Create to deploy your new compute instance.

09 (Optional) To remove the outdated compute instance from your Azure Machine Learning workspace, select the instance that you want to remove, select Delete, and choose again Delete in the confirmation box.

01 Run account list command (Windows/macOS/Linux) with custom output filters to list the IDs of the cloud subscriptions available in your Azure cloud account:

az account list
	--query '[*].id'

02 The command output should return the requested subscription identifiers (IDs):

[
	"abcdabcd-1234-abcd-1234-abcdabcdabcd",
	"abcd1234-abcd-1234-abcd-abcd1234abcd"
]

03 Run account set command (Windows/macOS/Linux) with the ID of the Azure cloud subscription that you want to access as the identifier parameter to set the selected subscription to be the current active subscription (the command does not produce an output):

az account set
	--subscription abcdabcd-1234-abcd-1234-abcdabcdabcd

04 Run ml workspace list command (Windows/macOS/Linux) with custom output filters to list the name and the associated resource group for each Azure Machine Learning workspace available in the current subscription:

az ml workspace list
	--output table
	--query '[*].{name:name, resourceGroup:resourceGroup}'

05 The command output should return the requested Azure Machine Learning workspace identifiers:

Name                           ResourceGroup
------------------------       ------------------------------
tm-ml-project5-workspace       cloud-shell-storage-westeurope
tm-ml-production-workspace     cloud-shell-storage-westeurope

06 Machine Learning compute instances get the latest VM images at the time of provisioning. However, once a compute instance is deployed, it does not get actively updated, even though Microsoft releases new VM images on a monthly basis. While you can query an instance's operating system version, to stay up to date with the latest software updates and security patches, you must re-create your Machine Learning compute instances to get the latest OS image. Run ml compute create command (Windows/macOS/Linux) to create your new compute instance:

az ml compute create
	--name project5-new-instance01
	--size Standard_DS3_v2
	--type ComputeInstance
	--workspace-name tm-ml-project5-workspace
	--resource-group cloud-shell-storage-westeurope

07 The command output should return the configuration information available for the new compute instance:

{
	"created_on": "2025-10-02T12:30:43.855643+0000",
	"enable_node_public_ip": true,
	"enable_os_patching": false,
	"enable_root_access": true,
	"enable_sso": true,
	"id": "/subscriptions/abcdabcd-1234-abcd-1234-abcdabcdabcd/resourceGroups/cloud-shell-storage-westeurope/providers/Microsoft.MachineLearningServices/workspaces/tm-ml-project5-workspace/computes/project5-new-instance01",
	"last_operation": {
		"operation_name": "Create",
		"operation_status": "Succeeded",
		"operation_time": "2025-10-02T12:30:51.837Z",
		"operation_trigger": "User"
	},
	"location": "westeurope",
	"name": "project5-new-instance01",
	"network_settings": {
		"private_ip_address": "10.0.0.4",
		"public_ip_address": "xxx.xxx.xxx.xxx"
	},
	"os_image_metadata": {
		"current_image_version": "25.07.12",
		"is_latest_os_image_version": true,
		"latest_image_version": "25.07.12"
	},
	"provisioning_state": "Succeeded",
	"release_quota_on_stop": false,
	"resourceGroup": "cloud-shell-storage-westeurope",
	"services": [
		{
			"display_name": "Jupyter",
			"endpoint_uri": "https://project5-new-instance01.westeurope.instances.azureml.ms/tree/"
		},
		{
			"display_name": "Jupyter Lab",
			"endpoint_uri": "https://project5-new-instance01.westeurope.instances.azureml.ms/lab"
		}
	],
	"size": "Standard_DS3_v2",
	"ssh_public_access_enabled": false,
	"ssh_settings": {
		"admin_username": "azureuser",
		"ssh_port": "50000"
	},
	"state": "Running",
	"type": "computeinstance"
	}

08 (Optional) To remove the non-compliant compute instance from your Azure Machine Learning workspace, run ml compute delete command (Windows/macOS/Linux) to delete the specified compute instance (the command does not produce an output):

az ml compute delete
	--name project5-ml-instance01
	--workspace-name tm-ml-project5-workspace
	--resource-group cloud-shell-storage-westeurope
	--yes