1. Serverless ML Workflows with Azure Logic Apps


    Azure Logic Apps is a cloud service that helps you schedule, automate, and orchestrate tasks, business processes, and workflows when you need to integrate apps, data, systems, and services across enterprises or organizations. It's a remarkable platform for building serverless machine learning (ML) workflows which can react to various events and integrate with various Azure services like Azure Machine Learning, Azure Functions, and more, without having to manage any infrastructure.

    For Serverless ML Workflows, Azure Logic Apps can be combined with other Azure services including Azure Functions (for running custom code), Azure Event Grid or Service Bus (for messaging), Azure Cosmos DB (as a No SQL database), Azure Blob Storage (for storing large amounts of unstructured data), and Azure Machine Learning for training and deploying machine learning models.

    In a typical ML workflow, we might have the following steps:

    1. Data Collection: Trigger the workflow when new data arrives or a certain condition is met.
    2. Data Processing: Process and prepare the data for training, which might involve an Azure Function if custom code is needed.
    3. Model Training: Send the processed data to an Azure Machine Learning pipeline to train a model.
    4. Model Evaluation: Evaluate the trained model's performance.
    5. Deployment: If the model meets certain performance criteria, deploy it to an endpoint for inference.

    Below is a Pulumi program that sets up an Azure Logic App that could act as the orchestrator of such a workflow. The Logic App could be triggered, for example, by new data arriving in an Azure Blob Storage container and then running an Azure Machine Learning pipeline to train a model.

    Keep in mind that this is a template, and more detailed steps should be defined according to your specific ML workflow while using Azure's various connectors and managed identities for secure access.

    import pulumi import pulumi_azure_native as azure_native # First, create a resource group resource_group = azure_native.resources.ResourceGroup('resource_group', resource_group_name='ml_workflow_rg') # Then define a new Logic App Workflow logic_app_workflow = azure_native.logic.Workflow( "logicAppWorkflow", location='East US', resource_group_name=resource_group.name, definition={ "definition": { "$schema": "https://schema.management.azure.com/providers/Microsoft.Logic/schemas/2016-06-01/workflowdefinition.json#", "actions": { # Define your actions here # Example: a placeholder for a Machine Learning action "Train_Model": { "type": "Action", # You can define here the details on how to connect to Azure ML and trigger a pipeline } }, "triggers": { # Define your triggers here # Example: a placeholder for a trigger that listens to a blob storage "When_blob_is_added_or_updated": { "type": "Trigger", # You can define here the details on how to monitor a blob storage for new data } }, "contentVersion": "", "outputs": {}, "parameters": {}, "metadata": {} }, "location": "East US", "state": "Enabled", # Enable the Logic App }) # Don't forget to export the Logic App's ID pulumi.export('logic_app_id', logic_app_workflow.id)

    In this program, we:

    • Create a resource group for organizing related resources.
    • Define a Logic App workflow with a resource name ("logicAppWorkflow") and specify a location for where it will be hosted.
    • Set the definition with a schema that Azure Logic Apps require, including placeholders for actions and triggers. The actions should be detailed based on the specific tasks needed for the ML workflow, such as invoking an Azure Function or Azure ML pipeline, and similarly, triggers should be updated with details on how to trigger this Logic App (like monitoring a blob storage).
    • Finally, we export the Logic App's ID, so we can identify it later in the Azure portal or use it in other parts of a larger Pulumi program.

    Please note, this Logic App definition is very high-level and serves as a template. It should be fleshed out with specific details of the ML workflow, triggers, and actions in actual implementation. Each action and trigger has its own specifics that need to be considered and designed according to the ML tasks you need to accomplish.