1. Performance Metrics Analysis for LLMs with Azure


    To monitor and analyze performance metrics for Large Language Models (LLMs) on Azure, you would typically require a combination of services for logging, monitoring, analytics, and potentially machine learning for advanced analysis. Here, we’ll use Azure Monitor's capabilities, including Application Insights and Log Analytics workspaces, to gather, store, and analyze metrics. Additionally, we require the Azure Machine Learning service for any machine learning-based analysis.

    Azure provides Application Insights for application performance management and Azure Log Analytics for log-based performance monitoring. Both are part of the Azure Monitor suite and allow you to collect detailed performance and telemetry information.

    Here is a Pulumi program in Python that sets up a basic monitoring environment for a Large Language Model (LLM) service hosted on Azure. It involves creating an Azure Machine Learning Workspace, an Application Insights component for application performance monitoring, and a Log Analytics Workspace for log-based monitoring:

    import pulumi import pulumi_azure_native as azure_native # Set up the Resource Group resource_group = azure_native.resources.ResourceGroup('resource_group') # Set up the Azure Machine Learning Workspace machine_learning_workspace = azure_native.machinelearningservices.Workspace( 'machine_learning_workspace', resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.machinelearningservices.SkuArgs( name="Standard" ), workspace_name='myLMWorkspace' ) # Set up the Application Insights component for performance monitoring. application_insights_component = azure_native.insights.Component( 'application_insights_component', resource_group_name=resource_group.name, kind='web', application_type='web', location=resource_group.location ) # Set up the Log Analytics workspace for logs monitoring. log_analytics_workspace = azure_native.operationalinsights.Workspace( 'log_analytics_workspace', resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.operationalinsights.WorkspaceSkuArgs( name='PerGB2018' ) ) # Export the Azure Machine Learning Workspace URL for easy access pulumi.export('ml_workspace_url', machine_learning_workspace.workspace_url) # Export the Application Insights instrumentation key pulumi.export('app_insights_instrumentation_key', application_insights_component.instrumentation_key) # Export the Log Analytics Workspace ID for queries pulumi.export('log_analytics_workspace_id', log_analytics_workspace.workspace_id)


    • We start by importing necessary modules from Pulumi Azure Native SDK.
    • We create a resource group, which is a container that holds related resources for Azure solutions.
    • Next, we set up an Azure Machine Learning Workspace where LLMs can be hosted and managed.
      • We provide a SKU for our workspace. The chosen SKU ("Standard") will depend on your requirements.
    • After setting up the base for our LLM, we create an Application Insights component. Application Insights will collect and analyze performance and telemetry data from the application hosting our model.
    • Then, we establish a Log Analytics workspace, which is used to collect and analyze logs. This is where all performance logs from the LLM can be sent for analysis.
    • Finally, we export useful information such as the Machine Learning Workspace URL, Application Insights Instrumentation Key, and the Log Analytics Workspace ID so they can be easily accessed outside our Pulumi program.

    This program will need to run within your Pulumi project, which you can create by following Pulumi's get started guide. Once you've set up your Pulumi project and have authenticated with Azure, you can run this program using the Pulumi CLI and then push the configuration to Azure. The CLI will output the exported values such as URLs and keys that you can use to further interact with Azure resources.