1. Performance Benchmarking of AI Applications


    Benchmarking the performance of AI applications typically involves measuring various metrics, such as training time, inference latency, memory usage, and throughput on different infrastructures. To deploy an AI application and track its performance, cloud platforms offer specialized services and tools.

    Assuming you want to create an AI application on Azure, use the Azure Machine Learning Service to train and deploy your model. For performance benchmarking, Azure's Application Insights can be used to monitor the application and track performance metrics.

    Let's create a small Pulumi program that sets up an Azure Machine Learning service workspace and an online endpoint for serving real-time inferencing requests from the trained model. Additionally, we'll add Azure Application Insights for monitoring.

    Please note, this program assumes you have set up your Azure credentials for use with Pulumi. Here's a breakdown of the code:

    • Import necessary libraries for Azure and Pulumi.
    • Create an Azure resource group to contain all resources.
    • Provision an Azure Machine Learning service workspace.
    • Create an online endpoint for serving the model, with performance optimized settings.
    • Deploy Azure Application Insights to collect telemetry and monitor the performance of the AI application.

    Here's the Pulumi program written in Python:

    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native.machinelearningservices import Sku from pulumi_azure_native.machinelearningservices import OnlineEndpointArgs # Create an Azure resource group resource_group = azure_native.resources.ResourceGroup("ai_resource_group") # Create an Azure Machine Learning Workspace ml_workspace = azure_native.machinelearningservices.Workspace("ml_workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=Sku( name="Standard_DS3_v2" ), description="AML Workspace for Performance Benchmarking of AI Applications" ) # Create an Azure Machine Learning Online Endpoint # Online endpoints offer robust real-time serving capabilities online_endpoint = azure_native.machinelearningservices.OnlineEndpoint("ai_online_endpoint", resource_group_name=resource_group.name, location=resource_group.location, workspace_name=ml_workspace.name, # The identity is used for securely connecting services like Application Insights identity={ "type": "SystemAssigned" } ) # Create an Application Insights instance to monitor the application app_insights = azure_native.insights.Component("ai_app_insights", resource_group_name=resource_group.name, kind="web", application_type="web", location=resource_group.location) # Added an output for Application Insights Instrumentation Key output_insights_key = pulumi.Output.secret(app_insights.instrumentation_key) # Export the necessary values pulumi.export("resource_group_name", resource_group.name) pulumi.export("ml_workspace_name", ml_workspace.name) pulumi.export("online_endpoint_name", online_endpoint.name) pulumi.export("app_insights_instrumentation_key", output_insights_key)

    In this code, we define and deploy the necessary resources for running an AI application and track its performance. We first create a resource group that serves as a container for all the Azure resources. Next, we define a machine learning workspace with the required specifications where the setup, training, and deployment of AI models will take place.

    We then define an online endpoint for real-time inferencing, which will serve the AI model's predictions. After defining and provisioning these services, we deploy Azure Application Insights, which is an extensible Application Performance Management (APM) service for developers and DevOps professionals. We use it here to monitor our live applications; it will automatically detect performance anomalies and provide powerful analytics tools to help diagnose issues and understand what users actually do with the AI application.

    The output section at the end of the Pulumi program is used to reveal pertinent details about the created resources. The Application Insights Instrumentation Key is something you might use in your application code to send telemetry to Application Insights, which is why it's labeled as a secret.

    This program sets up the initial infrastructure but doesn't encompass the full process of training AI models or the benchmarking activities, as these steps would require additional components and potentially custom code within the AI application itself. Once your application is deployed and running, you can use Azure's monitoring tools to collect data and begin the benchmarking process, comparing different configurations and deployments.