1. Low-latency ML Inference with Edge Connectivity Solutions


    To achieve low-latency machine learning (ML) inference with edge connectivity solutions, you will typically deploy your ML models closer to your data sources and end-users. This helps to minimize the latency that can occur when data needs to travel over long distances to centralized data centers for processing. Azure provides a variety of services that can be utilized to create such a solution, including Azure Machine Learning (Azure ML) for managing and deploying ML models, and Azure IoT Edge for deploying cloud intelligence directly on IoT devices.

    Below is an outline of a Pulumi program, written in Python, that demonstrates how you can use these services to deploy a low-latency ML inference solution:

    1. Set up an Azure ML Workspace: This acts as the centralized hub for all machine learning activities, including model training, model management, and model deployment.

    2. Create an Azure ML Inference Cluster: A cluster dedicated to running your machine learning models for inference.

    3. Register a Machine Learning Model: Upload and register your pre-trained machine learning model to the Azure ML Workspace.

    4. Deploy the Model as a Web Service: Deploy the registered model to the inference cluster, exposing it as a web service to receive data and return predictions.

    5. Set up Azure IoT Edge: Configure an IoT Edge device that can run ML models at the edge, which is useful when immediate inference is crucial or when there's limited connectivity to the cloud.

    Here's a Pulumi program that sets up such an infrastructure:

    import pulumi import pulumi_azure_native as azure_native # 1. Create Azure ML Workspace ml_workspace = azure_native.machinelearningservices.Workspace( "mlWorkspace", location="East US", resource_group_name="myResourceGroup", # Ensure that the resource group is already created or managed elsewhere in your Pulumi program sku=azure_native.machinelearningservices.SkuArgs( name="Standard" ), workspace_name="myMachineLearningWorkspace" ) # 2. Create an Inference Cluster (AKS) inference_cluster = azure_native.machinelearningservices.Computes( "inferenceCluster", compute_name="myCluster", location="East US", properties=azure_native.machinelearningservices.AKSPropertiesArgs( agent_count=3, agent_vm_size="Standard_DS3_v2", # Choose an appropriate VM size based on the expected workload. cluster_purpose=azure_native.machinelearningservices.AksComputeClusterPurposeArgs.DEDICATED, ), resource_group_name="myResourceGroup", workspace_name=ml_workspace.name ) # 3. Register a Machine Learning Model model = azure_native.machinelearningservices.Model( "myModel", model_name="myMLModel", resource_group_name="myResourceGroup", workspace_name=ml_workspace.name, properties=azure_native.machinelearningservices.ModelPropsArgs( # Model properties such as location of the model, description, etc. # This assumes the model file is already uploaded in a location accessible by the workspace ) ) # 4. Deploy the Model as a Web Service model_service = azure_native.machinelearningservices.EndpointVariant( "modelService", endpoint_name="myModelService", resource_group_name="myResourceGroup", workspace_name=ml_workspace.name, properties=azure_native.machinelearningservices.OnlineEndpointTypeArgs( compute_type="Managed", # Managed service for easier maintenance. # Other properties for deployment configuration. ) ) # 5. Configure Azure IoT Edge Device # Please note that IoT Edge deployments can get complex and might require additional steps. # The following is a simplified example and assumes you have a pre-configured IoT Edge instance. iot_edge_device = azure_native.devices.Device( "iotEdgeDevice", capabilities=azure_native.devices.DeviceCapabilitiesArgs( iot_edge=True ), location="East US", resource_group_name="myResourceGroup" ) # Export the endpoint URL of the deployed web service to interact with it later. pulumi.export("inference_endpoint_url", model_service.properties.apply(lambda props: props.scoring_uri))

    This program provides a basic framework, and further configuration will be needed based on the specific requirements of your ML solution, including security considerations, model specifics, and scalability requirements.

    The use of Azure ML Workspace, Inference Cluster, and Model registration helps keep your ML lifecycle managed and scalable. Deploying the model as a service makes it easily accessible for applications and services that require inference. For the Edge solution, Azure IoT Edge brings compute capabilities to devices, allowing faster responses and decision-making locally.

    Please make sure that you have the necessary Azure permissions and Pulumi configuration to deploy these resources, and remember to replace placeholder values with actual ones that suit your use case.