1. AI Workflow Monitoring with Azure Operational Insights Workspace


    To set up an AI Workflow Monitoring solution with Azure Operational Insights Workspace, also known as Log Analytics Workspace, using Pulumi in Python, follow these steps:

    1. Create an Azure Resource Group: A resource group is a container that holds related resources for an Azure solution.

    2. Create an Azure Operational Insights Workspace: This resource provides a workspace for your AI monitoring data.

    3. Link Storage Accounts or Other Services if Necessary: If you need to collect data from Azure Storage Accounts or other services, you'll link these to your workspace.

    4. Set Up Data Sources or Solutions: You might need to set up additional data sources or solutions within your workspace, depending on what exactly you want to monitor.

    5. Export Data for Analysis if Required: In some cases, you might need to export data from your Operational Insights workspace to another location for further analysis.

    Let's write the Pulumi program step by step with explanations:

    import pulumi import pulumi_azure_native as azure_native # Replace these variables with your own desired names and settings resource_group_name = "ai_workflow_monitoring_rg" location = "East US" workspace_name = "ai_workflow_insights_workspace" # Step 1: Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup("resource_group", resource_group_name=resource_group_name, location=location) # Step 2: Create an Azure Operational Insights Workspace # The workspace is where logs and metrics are collected and analyzed. workspace = azure_native.operationalinsights.Workspace("workspace", resource_group_name=resource_group.name, workspace_name=workspace_name, location=resource_group.location, sku=azure_native.operationalinsights.SkuArgs(name="PerGB2018")) # Step 3: Optionally link Storage Accounts or other services # Here you would use `LinkedStorageAccount` or other related resources # to connect your storage accounts or services to the insights workspace. # Step 4: Set Up Data Sources or Solutions # Depending on your monitoring requirements, you might need to create additional data sources. # This could be achieved using `DataSource` resource from the `azure_native.operationalinsights` module. # Step 5: Export Data for Analysis if Required # If needed, set up data export using `DataExport` resource to periodically export data to an # Azure Storage Account or Azure Event Hub for longer-term storage or additional analysis. # (Optional) Export the ID of the workspace, which might be required for other operations or tracking purposes. pulumi.export("workspace_id", workspace.id)


    • Resource Group: A logical container for Azure resources. Creating a resource group helps manage and organize your Azure resources.
    • Operational Insights Workspace: This is the main component where data will be collected, aggregated, analyzed, and presented in Azure Monitor Log Analytics.
    • SKU for Workspace: We've selected "PerGB2018" for pricing and retention purposes, but this can be changed according to your needs.
    • Data Sources: For AI workflows, you may need to collect data from various sources. Data sources specify where from and how the data should be collected.
    • Data Export: This is an optional step. If there's a need to send data elsewhere for additional processing or storage, you'll set up data export rules.

    Keep in mind that depending on your specific monitoring needs, you may need to configure additional resources or adjust settings within the resources outlined above. This program is a foundational start for an AI workflow monitoring set up on Azure.