Event-driven ML Model Scoring with Azure Event Hubs

Question

Pulumi · Accepted Answer

To build an event-driven machine learning (ML) model scoring system with Azure Event Hubs, you'll need to create a data pipeline that:

1. Collects events (data for scoring) through Azure Event Hubs.
2. Processes and scores the data using an Azure Function with ML capabilities, such as Azure Machine Learning or other custom logic.
3. Optionally stores or logs the output of the scoring process for further usage or analysis.

Here's an overview of the Pulumi resources you'll use to accomplish this:

- `azure-native.eventhub.EventHub`: To create an Event Hub that will receive the events for ML scoring.
- `azure-native.eventhub.Namespace`: An Event Hubs Namespace which is a container for multiple Event Hubs.

You can further extend this basic setup by integrating with Azure Machine Learning for model registry and scoring, using Azure Functions to execute the scoring logic, and tying other services such as Azure Cosmos DB or Azure Blob Storage to store the results.

Below you will find a Pulumi program written in Python which sets up an Azure Event Hubs Namespace and an Event Hub within it. Post-deployment, you would deploy your Azure Function to connect to the Event Hub and implement the scoring logic.

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure Resource Group
resource_group = azure_native.resources.ResourceGroup("resource_group")

# Create an Event Hubs Namespace
event_hub_namespace = azure_native.eventhub.Namespace("eventHubNamespace",
                                                      resource_group_name=resource_group.name,
                                                      location=resource_group.location,
                                                      sku=azure_native.eventhub.SkuArgs(
                                                          name="Standard"  # Standard tier should suffice for most scenarios
                                                      ),
                                                      tags={"environment": "production"})

# Create an Event Hub within the namespace
event_hub = azure_native.eventhub.EventHub("eventHub",
                                           resource_group_name=resource_group.name,
                                           namespace_name=event_hub_namespace.name,
                                           partition_count=4,  # Adjust the partition count depending on the throughput needs
                                           message_retention_in_days=1)  # Retention policy can be adjusted as needed

# Export the Event Hub connection string (primary key) to use with the Azure Function
primary_key_output = event_hub_namespace.name.apply(
    lambda namespace_name: azure_native.eventhub.list_namespace_keys(
        alias=namespace_name,
        resource_group_name=resource_group.name,
        authorization_rule_name="RootManageSharedAccessKey"  # Default rule created with namespace
    ))

connection_string_output = primary_key_output.apply(lambda keys: keys.primary_connection_string)

pulumi.export('event_hub_connection_string', connection_string_output)
```

In the above program:

- A new resource group is created to contain all the resources.
- An Event Hubs Namespace is declared with a standard pricing tier which would provide a balanced performance for ML scoring scenarios.
- An Event Hub is then created within this namespace. The `partition_count` directly relates to the scale and throughput of the Event Hub.
- `message_retention_in_days` is set to 1, which means messages are retained for one day before they're automatically deleted. This is a critical setting if you have requirements regarding how long raw input data is stored.
- The `list_namespace_keys` function fetches the primary key for the namespace which is needed to connect producers and consumers, like an Azure Function, to the Event Hub.

After the deployment with Pulumi, you would proceed with writing your Azure Function in VS Code or another IDE of your choice. The function would use the Event Hub trigger to process incoming events and execute the machine learning model scoring. The connection string exported above will be used to configure the Azure Function's Event Hub trigger connection.

The specifics of the Azure Function and its integration with an ML model scoring service are beyond the scope of infrastructure deployment, but should you need instructions or guidance on writing the function and connecting it with Event Hubs, you may refer to the Azure Functions and Event Hubs documentation on the Microsoft Azure website.

Please ensure that you have the Pulumi CLI installed and configured for use with Azure. You can install the CLI from [Pulumi's website](https://www.pulumi.com/docs/get-started/azure/) and follow the instructions to log in to Azure with `pulumi login`. Once you're logged in, this code can be run using the Pulumi CLI to provision the resources specified.