1. Logging AI Model Inference Metrics in Azure


    Logging AI Model Inference Metrics in Azure involves capturing the data that your machine learning model generates when it runs in production. These data points could be the input data, the predictions, and any other metrics such as latency, throughput, etc. Logging these metrics in Azure can be done using several services, including Azure Machine Learning and Azure Monitor.

    For this purpose, we can leverage the azure-native.machinelearningservices.InferenceEndpoint to deploy and monitor an inference endpoint for logging the inference metrics. This resource allows you to set up an endpoint that can be used to deploy machine learning models as web services on Azure, and inherently allows logging of metrics.

    Additionally, you could use azure-native.insights.GuestDiagnosticsSetting for more fine-grained control over logging and capturing guest-level diagnostic data like performance metrics and event logs, especially if you have deployed your model on an Azure VM.

    In this program:

    • We will create an inference endpoint using the Azure Machine Learning services.
    • Set up monitoring for the endpoint through Azure Monitor.

    Here is a Pulumi Python program that sets up an inference endpoint and configures diagnostics settings to log model inference metrics:

    import pulumi import pulumi_azure_native.machinelearningservices as mls import pulumi_azure_native.insights as insights # Define the required resource group and machine learning workspace resource_group_name = 'my-resource-group' workspace_name = 'my-ml-workspace' # Define an Inference Endpoint inference_endpoint = mls.InferenceEndpoint("my-inference-endpoint", resource_group_name=resource_group_name, workspace_name=workspace_name, location="East US", # Specify the Azure location inference_endpoint_properties=mls.InferenceEndpointPropertiesArgs( # Set up properties according to the needs of your model auth_mode="Key", # Choose an authentication mode, e.g., key-based or token-based # Other properties can be set here such as a description, compute type, etc. ) ) # Configure diagnostics settings diagnostics_setting = insights.GuestDiagnosticsSetting("my-diagnostics-setting", resource_group_name=resource_group_name, location="East US", os_type="Linux", # Assuming a Linux VM for the inference endpoint data_sources=[insights.DataSourceArgs( kind="Linux", configuration=insights.LinuxConfigurationArgs( # Define the diagnostics data types to collect, e.g., performance counters, system logs performance_counters=[insights.PerformanceCounterArgs( name="CPU usage", sampling_period="PT1M", # Sampling period (ISO 8601 format), e.g., PT1M for 1 minute )], # More configurations can be added as required. ) )] ) # Output the ID of the inference endpoint, useful for accessing and interfacing with the service pulumi.export("inference_endpoint_id", inference_endpoint.id) # Output the diagnostic setting resource ID, useful for tracking and managing diagnostics pulumi.export("diagnostics_setting_id", diagnostics_setting.id)

    This program sets up an inference endpoint where you would deploy your AI model and a diagnostics setting to log the performance metrics. In the data_sources section of GuestDiagnosticsSetting, you specify what you want to log. For instance, here we have an example of how to collect CPU usage metrics at a 1-minute sampling period.

    To extend this, you can log other metrics or custom event data by adjusting the properties of the GuestDiagnosticsSetting. The diagnostics data can be sent to an Azure Storage account, Event Hub, or Log Analytics workspace for analysis and visualization.

    Remember to replace the placeholders such as resource_group_name and workspace_name with your specific resource group and workspace names. Additionally, you should customize the inference_endpoint_properties and data_sources based on the metrics you are interested in collecting.