1. Implementing A/B testing for models using Azure Machine Learning endpoints

    TypeScript

    Implementing A/B testing for models using Azure Machine Learning endpoints requires setting up multiple models to serve different versions of your application and then routing incoming requests to different models. In Azure, this can be achieved using Azure Machine Learning Service which allows you to create and manage machine learning models, and you can deploy these models as web services.

    To implement A/B testing, we would set up two or more model endpoints, each serving a different model version, and then configure the traffic routing to send a certain percentage of requests to each model. We can do this using the OnlineEndpoint resource from the Azure Machine Learning Service, which we can define and manage using Pulumi.

    The following Pulumi TypeScript program demonstrates how to set up a basic A/B testing scenario using Azure Machine Learning endpoints:

    1. Define two machine learning models.
    2. Deploy them as separate Azure Machine Learning Online Endpoints.
    3. Configure traffic routing to allocate a percentage of the traffic to each endpoint.
    import * as pulumi from "@pulumi/pulumi"; import * as azure_native from "@pulumi/azure-native"; const resourceGroup = new azure_native.resources.ResourceGroup("myResourceGroup"); const workspace = new azure_native.machinelearningservices.Workspace("myWorkspace", { resourceGroupName: resourceGroup.name, location: resourceGroup.location, sku: { name: "Standard", }, }); // Model A const modelA = new azure_native.machinelearningservices.ModelVersion("modelA", { modelName: "myModelA", version: "1", workspaceName: workspace.name, resourceGroupName: resourceGroup.name, modelVersionProperties: { description: "Model A for A/B testing", modelUri: "<URI of the model A stored in Azure Blob>", }, }); // Model B const modelB = new azure_native.machinelearningservices.ModelVersion("modelB", { modelName: "myModelB", version: "1", workspaceName: workspace.name, resourceGroupName: resourceGroup.name, modelVersionProperties: { description: "Model B for A/B testing", modelUri: "<URI of the model B stored in Azure Blob>", }, }); // Online Endpoint for Model A const onlineEndpointA = new azure_native.machinelearningservices.OnlineEndpoint("onlineEndpointA", { location: resourceGroup.location, workspaceName: workspace.name, resourceGroupName: resourceGroup.name, onlineEndpointProperties: { description: "Online Endpoint for Model A", // Configure properties specific to the online endpoint }, }); // Online Deployment for Model A const onlineDeploymentA = new azure_native.machinelearningservices.OnlineDeployment("onlineDeploymentA", { location: resourceGroup.location, workspaceName: workspace.name, endpointName: onlineEndpointA.name, resourceGroupName: resourceGroup.name, onlineDeploymentProperties: { // Configure model, compute, and other deployment properties here }, }); // Online Endpoint for Model B const onlineEndpointB = new azure_native.machinelearningservices.OnlineEndpoint("onlineEndpointB", { location: resourceGroup.location, workspaceName: workspace.name, resourceGroupName: resourceGroup.name, onlineEndpointProperties: { description: "Online Endpoint for Model B", // Configure properties specific to the online endpoint }, }); // Online Deployment for Model B const onlineDeploymentB = new azure_native.machinelearningservices.OnlineDeployment("onlineDeploymentB", { location: resourceGroup.location, workspaceName: workspace.name, endpointName: onlineEndpointB.name, resourceGroupName: resourceGroup.name, onlineDeploymentProperties: { // Configure model, compute, and other deployment properties here }, }); // Set traffic for A/B testing (e.g., 50% to A, 50% to B) const trafficConfig = new azure_native.machinelearningservices.OnlineEndpointTraffic("trafficConfig", { endpointName: onlineEndpointA.name, workspaceName: workspace.name, resourceGroupName: resourceGroup.name, traffic: pulumi.all([onlineDeploymentA, onlineDeploymentB]).apply(([deploymentA, deploymentB]) => { return { [deploymentA.name]: 50, [deploymentB.name]: 50, }; }), });

    In this program, the ModelVersion resource is used to define the two models for A/B testing. Each model has a corresponding OnlineEndpoint which represents the web service through which the model is exposed. OnlineDeployment resources then deploy the respective models to their endpoints.

    Finally, the OnlineEndpointTraffic resource configures the traffic distribution between the two models for A/B testing, with a 50-50 split represented by the traffic object.

    Remember to replace <URI of the model A stored in Azure Blob> and <URI of the model B stored in Azure Blob> with the actual URI of your models stored in Azure Blob Storage.

    This setup establishes the infrastructure for A/B testing; however, A/B testing itself involves more than just infrastructure, such as experiment design, data collection, and analysis of results to evaluate the performance of each model version. This code provides the foundational Azure Machine Learning infrastructure needed to conduct an A/B test using Pulumi.


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