AI Workflow Visualization with Azure Resource Graph

Question

Pulumi · Accepted Answer

When building or managing Azure cloud infrastructure, you may need to visualize and interact with the resources you have deployed. Azure Resource Graph is a service in Azure that allows you to explore your Azure resources at scale, query across multiple subscriptions, and stay compliant with your corporate standards.

The Azure Resource Graph offers a way to discover, explore, and query your resources efficiently and at scale. When you use the Resource Graph, you can:

1. Find resources that match certain criteria.
2. Assess the impact of applying policies across various scopes.
3. Detect changes that occurred to the resources.
4. Relate resources to one another for networking and security purposes.

Now, let's write a Pulumi program that demonstrates how to use the Azure Resource Graph to visualize AI workflows. In this program, we will query Azure resources that are related to AI (like Azure Machine Learning workspaces or resources involved in a machine learning pipeline).

For this example, we'll use `azure-native.resourcegraph.GraphQuery` to execute a query against the Azure Resource Graph. Here, the `GraphQuery` resource represents a saved query which can be executed to retrieve resource information.

```python
import pulumi
import pulumi_azure_native.resourcegraph as resourcegraph

# Construct a query to find Azure resources related to AI such as Machine Learning Workspaces
ai_resource_query = """Resources
| where type contains 'machinelearning'
| project name, location, type, tags
"""

# Create a Resource Graph Query using the AI resource query
ai_workflow_visualization_query = resourcegraph.GraphQuery("aiWorkflowVisualizationQuery",
    resource_group_name="your_resource_group", # Replace with your resource group name
    location="westus",                          # Replace with the location that fits your setup
    query=ai_resource_query,
    description="Query to visualize AI workflows in the Azure Resource Graph"
)

# Export the query result
pulumi.export('ai_workflow_visualization_query_id', ai_workflow_visualization_query.id)
```

In this code:
- We're creating a `GraphQuery` with a specific Kusto Query Language (KQL) string designed to search for Azure resources related to machine learning.
- The `resource_group_name` and `location` should be replaced with the actual resource group and location you want to run the query in.
- The `query` property is assigned our KQL string, which will be executed when we want to visualize our AI workflows.
- The `GraphQuery` resource is given a descriptive name and a brief description to explain what the query is for.

To run this code, you need a Pulumi account, the Pulumi CLI installed and configured to communicate with your Azure account, and the necessary permissions to read the Azure resources and create a `GraphQuery` resource.

Once you have executed this Pulumi program, it will output the ID of the `GraphQuery`. You can then head to the Azure portal to run the query in the Resource Graph Explorer and visualize your AI workflows. Alternatively, you could programmatically execute the query and handle the results within your Pulumi program or another application.

Remember, this code won't directly provide a visualization; instead, it prepares a query that, when executed, will fetch the data you can then visualize using Azure Portal's Resource Graph Explorer or other tools that support KQL results.