1. Streamlined Deployment of Machine Learning Microservices


    To streamline the deployment of machine learning microservices, you typically need to complete a number of steps, such as creating a workspace for managing your resources, registering your machine learning models, setting up compute resources to run the models, and finally deploying your models as web services for inference.

    For this example, let's assume we're working with Azure as our cloud provider, and we're going to deploy machine learning microservices using Azure Machine Learning Services.

    Here are the key resources we will use:

    • Machine Learning Workspace: A central resource in Azure Machine Learning that provides a place for data scientists to work on various aspects of data science and machine learning.

    • Machine Learning Compute Instance: This is the compute resource to run experiments and train models.

    • Machine Learning Model: The model resource that will be registered in Azure Machine Learning service, providing versioning and a managed resource for the models.

    • Machine Learning Online Deployment: A deployment of the model as an HTTP endpoint for real-time scoring.

    Let's begin constructing our deployment program using Pulumi in Python:

    import pulumi import pulumi_azure_native.machinelearningservices as ml # Initialize a Pulumi stack for our deployment stack_name = "ml-microservices-deployment" # Define the Azure Resource Group resource_group = ml.ResourceGroup("rg", resource_group_name=stack_name) # Create a Machine Learning Workspace ml_workspace = ml.Workspace("mlWorkspace", resource_group_name=resource_group.name, workspace_name=stack_name, location="East US", sku=ml.SkuArgs(name="Standard") ) # Here you would define your Compute resources, like AzureML Compute Clusters # Note: This is placeholder code and needs to be adjusted for actual requirements ml_compute = ml.AmlCompute("mlCompute", resource_group_name=resource_group.name, workspace_name=ml_workspace.name, compute_name="gpu-cluster", properties=ml.AmlComputePropertiesArgs( vm_size="STANDARD_NC6", vm_priority="dedicated", scale_settings=ml.ScaleSettingsArgs( max_node_count=1, ), ) ) # Registering a machine learning model # Note: In a real-world scenario, you would upload the model file or Docker image # into Azure Blob storage or Container Registry and pass its URI in the `model_uri` below. model_uri = "azure://<container-uri>/model" ml_model = ml.Model("mlModel", resource_group_name=resource_group.name, workspace_name=ml_workspace.name, model_name="my-model", properties=ml.ModelPropertiesResourceArgs( model_uri=model_uri ) ) # Deploy the model as an online service # Note: For the actual scoring file and environment configuration, you would need to provide proper AzureML Environment and InferenceConfig ml_online_deployment = ml.OnlineDeployment("mlOnlineDeployment", resource_group_name=resource_group.name, workspace_name=ml_workspace.name, endpoint_name=stack_name + "-endpoint", deployment_name="my-endpoint-deployment", kind="AKS", properties=ml.OnlineDeploymentPropertiesArgs( # example that you need to modify according to your actual scoring file and environment inference_config=ml.InferenceConfigArgs( entry_script="score.py", source_directory="./src", environment=ml.EnvironmentArgs( name="my-env" ), ), deployment_target=ml.ComputeTargetArgs( compute_type="AmlCompute", target="gpu-cluster" ) ) ) # Exporting the endpoint URL for client applications pulumi.export("endpoint_url", ml_online_deployment.endpoint_url)

    In this program, we create a resource group and a workspace, register a machine learning model, create a compute target, and deploy the model with an online endpoint.

    The mlWorkspace is the main Azure ML resource management entity. The mlCompute is a placeholder for your training compute, which is typically an AzureML Compute Cluster that you would set up for your training needs. The mlModel is the actual machine learning model which you would train, version, and manage. Finally, the mlOnlineDeployment is the deployment of the model, which you expose as a service endpoint.

    Please note that there are placeholders in this code where you would insert your actual infrastructure details, such as model URIs, compute specifications, and inference configurations. Additional configurations such as autoscaling, authentication, and monitoring settings would also need to be included for a production deployment.