1. Automated Machine Learning Model Deployment with TLS Certificates


    In order to deploy an automated machine learning model with TLS certificates, we’ll focus on creating a secure endpoint that can serve the model for inference. For this example, we can use the Azure Machine Learning (AML) service, which provides a complete workflow to train, deploy, and manage machine learning models.

    With Azure Machine Learning, we can deploy the machine learning model as a web service to containers running in Azure Kubernetes Service (AKS), which can be secured using TLS certificates. The process generally involves:

    1. Registering the model in the AML workspace.
    2. Creating a scoring script that the web service will call to run the model.
    3. Creating an environment for the model specifying necessary dependencies.
    4. Creating an inference configuration with the scoring script and environment.
    5. Deploying the model to an AKS cluster with SSL enabled to secure the endpoint.

    Below is a Pulumi program in Python that illustrates how these steps could be automated, assuming you already have a trained machine learning model, a scoring script, and knowledge of the necessary dependencies for your model:

    Please note that this is a high-level illustration. Actual machine learning deployment may require more specific configurations based on the model and requirements.

    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native import machinelearningservices, resources # Set up configuration variables resource_group_name = 'myResourceGroup' workspace_name = 'myMachineLearningWorkspace' model_name = 'myModel' scoring_script_file = './score.py' environment_file = './environment.yml' # Set up resource group resource_group = resources.ResourceGroup(resource_group_name) # Create the Azure Machine Learning workspace workspace = machinelearningservices.Workspace( 'workspace', resource_group_name=resource_group.name, location=resource_group.location, sku={ "name": "Basic", }, ) # Registering the model in the AML workspace registered_model = machinelearningservices.Model( 'registeredModel', resource_group_name=resource_group.name, workspace_name=workspace_name, model_name=model_name, properties={ "modelName": model_name, "description": "A registered model for deployment.", "modelUri": f"azureml://{model_name}/versions/1", # Specify the path to your model file }, opts=pulumi.ResourceOptions(parent=workspace), ) # Define the scoring script scoring_script = pulumi.FileAsset(scoring_script_file) # Define the environment file with dependencies environment_file = pulumi.FileAsset(environment_file) # Set up the inference configuration inference_config = machinelearningservices.InferenceConfig( "inferenceConfig", source_directory=".", entry_script=scoring_script, environment=environment_file, runtime="python", # Depending on the model, this could be 'python', 'spark-py', or 'docker' conda_file=environment_file, opts=pulumi.ResourceOptions(parent=workspace), ) # Deploy the model to the AKS cluster with TLS enabled # Note: This assumes an Azure Kubernetes Service cluster 'aks_cluster' has been set up with SSL/TLS. aks_target = machinelearningservices.AksCompute( "aksTarget", resource_group_name=resource_group.name, workspace_name=workspace_name, compute_name='myAksCluster', properties={ "agent_count": 3, "agent_vm_size": "Standard_D3_v2", "cluster_purpose": "DevTest", "ssl_configuration": { # Make sure to have a valid SSL certificate for TLS "status": "Enabled", "cert": "<CERTIFICATE_CONTENT>", "key": "<CERTIFICATE_KEY>", "cname": "<CNAME_FOR_SSL>", }, }, opts=pulumi.ResourceOptions(parent=workspace), ) # After deploying to AKS, get the endpoint URL endpoint = pulumi.Output.all(workspace.name, workspace.location, registered_model.name, aks_target.name).apply( lambda args: f"https://{args[0]}.azurewebsites.net/score?model_name={args[2]}&version=1" ) # Export the scoring endpoint URL pulumi.export('scoring_endpoint', endpoint)

    This Pulumi program will set up all the necessary resources, including a secure endpoint with TLS certificates to deploy the machine learning model on Azure. It takes care of creating a workspace for machine learning, registering the model, setting up the inference configuration, and deploying it to an Azure Kubernetes Service with the necessary SSL/TLS configurations.

    The TLS certificates (<CERTIFICATE_CONTENT> and <CERTIFICATE_KEY>) and the CNAME (<CNAME_FOR_SSL>) should be obtained and managed securely, as they contain sensitive cryptographic material. This example hardcodes them for simplicity, but in a production scenario, you might want to pull these values from a secure configuration store or a key vault.

    At the end of the script, the scoring endpoint URL is exported. This is the URL clients can send data to for inference using the deployed model, under a secured connection provided by the TLS certificates.

    Remember to replace placeholders like <CERTIFICATE_CONTENT>, <CERTIFICATE_KEY>, <CNAME_FOR_SSL>, and the model URI azureml://{model_name}/versions/1 with your actual values. Also, ensure the scoring script is correctly pointed to by updating scoring_script_file, and dependencies are correctly listed in environment_file.