Orchestrating AI Workflows with Azure Managed Applications

Question

Pulumi · Accepted Answer

To orchestrate AI workflows with Azure Managed Applications using Pulumi, you can use various Azure services that complement each other to establish a robust AI workflow environment. The main services one might consider integrating are Azure Machine Learning for training and managing machine learning models, Azure Batch for large-scale parallel and batch computing workflows, Azure Logic Apps for automating workflows, and Azure Functions for event-driven serverless compute.

Here, we'll create an Azure Managed Application infrastructure with these services using Pulumi's `azure-native` package. We'll start with an Azure Resource Group, which will act as a container for all our resources. Then, we'll provision an Azure Machine Learning Workspace to host our AI models, experiments, and pipelines. Azure Batch will help us run large-scale parallel and batch processing pipelines, and Azure Functions will allow us to execute code in response to events, like model retraining triggers. We'll also add an Azure Logic App as a mechanism for orchestrating these resources and processes. This program assumes that you have the necessary permissions and Azure SPN (Service Principal Name) configured to create these resources.

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure Resource Group to contain all the resources
resource_group = azure_native.resources.ResourceGroup("ai_workflow_resource_group")

# Create an Azure Machine Learning Workspace
ml_workspace = azure_native.machinelearningservices.Workspace(
    "ai_ml_workspace",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    identity=azure_native.machinelearningservices.IdentityArgs(
        type="SystemAssigned"  # Utilizing Managed Identity for secure access
    ),
)

# Create an Azure Batch Account to handle large-scale parallel and batch workloads
batch_account = azure_native.batch.Account(
    "ai_batch_account",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    identity=azure_native.batch.AccountIdentityArgs(
        type="SystemAssigned"
    ),
    pool_allocation_mode="BatchService",
)

# Create an Azure Function App, which we can use to execute code in response to events
function_app = azure_native.web.FunctionApp(
    "ai_function_app",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    identity=azure_native.web.ManagedServiceIdentityArgs(
        type="SystemAssigned"
    ),
)

# Create an Azure Logic App for workflow automation
logic_app = azure_native.logic.Workflow(
    "ai_logic_app",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    definition={
        # Define the logic app workflow as required, for example,
        # a workflow that triggers retraining of an ML model on a schedule or an event
        # You'll need to define your specific workflow logic here
    },
    parameters={},
)

# Expose some of the important URLs and IDs as stack outputs
pulumi.export('resource_group_name', resource_group.name)
pulumi.export('ml_workspace_name', ml_workspace.name)
pulumi.export('batch_account_name', batch_account.name)
pulumi.export('function_app_name', function_app.name)
pulumi.export('logic_app_name', logic_app.name)
```

In this program, we first create an Azure Resource Group called `ai_workflow_resource_group`. This serves as a grouping for all our Azure resources. Next, we set up the Azure Machine Learning Workspace with the name `ai_ml_workspace`, which is an essential component of Azure's AI services, allowing for training, storing, and managing machine learning models. We also introduce Azure Batch Account `ai_batch_account` to manage compute resources for running large-scale parallel and high-performance computing workloads. The `ai_function_app` is an Azure Function App which can be used for serverless compute, executing code in response to various triggers. Lastly, `ai_logic_app` is where you would define your workflow automation using Azure Logic Apps, which could range from scheduling tasks to orchestrating complex workflows involving multiple Azure services.

We export the names of our resources using `pulumi.export` to make them accessible outside of our Pulumi program. These outputs could be used in CI/CD pipelines or other automation workflows.

This Pulumi program sets the stage for you to create AI workflows leveraging Azure services. You'd further define your machine learning pipelines, set rules and triggers for your function apps, and articulate the workflow in the logic app to fit your specific AI workload requirements.