Metadata Management for Machine Learning with Azure Purview

Question

Pulumi · Accepted Answer

Metadata management is an essential part of any data-intensive application or service, such as those involving Machine Learning (ML). It involves handling the metadata—which is data about other data—ensuring that it is well organized and accessible. This is crucial for ML processes, where you need to keep track of various datasets, models, experiments, and runs.

Azure Purview is a unified data governance service that helps you manage and govern your on-premises, multicloud, and software-as-a-service (SaaS) data. With Azure Purview, you can automate the discovery of data and catalog the data while managing its governance across your enterprise landscape. It is not limited to metadata management for ML, but it provides a comprehensive suite of tools for managing the metadata efficiently.

In terms of managing metadata for ML with Azure, Azure Machine Learning (Azure ML) service provides capabilities for tracking experiments, datasets, and models, which is typically what is required in ML workflows. Azure Machine Learning has components like workspaces, datasets, models, and experiments that help with organizing and managing ML-related metadata. It is possible to integrate Azure ML with Azure Purview to have more centralized governance and metadata management.

Below is a Pulumi program written in Python that sets up an Azure Machine Learning Workspace with Azure Purview integration by creating the workspace and registering it with Azure Purview. This program is not exhaustive, but it should give you a starting point for creating and managing ML metadata with Azure services using Pulumi.

```python
import pulumi
import pulumi_azure as azure

# Replace the following variables with your own values
purview_account_name = 'my-purview-account'
resource_group_name = 'my-resource-group'
location = 'East US'
ml_workspace_name = 'my-ml-workspace'

# Create an Azure Resource Group if it does not already exist
resource_group = azure.core.ResourceGroup('my-resource-group',
    location=location)

# Create an Azure Purview account
purview_account = azure.purview.Account('my-purview-account',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku=azure.purview.AccountSkuArgs(
        capacity=4,  # Minimal capacity is 4
        name='Standard'
    ),
    public_network_enabled=True)

# Create an Azure ML Workspace
ml_workspace = azure.machinelearning.Workspace('my-ml-workspace',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku='standard',
    identity=azure.machinelearning.WorkspaceIdentityArgs(
        type='SystemAssigned'  # Using a system-assigned managed identity
    ))

# Now we need to link the Purview account with the Machine Learning Workspace. 
# As of my last knowledge, linking the services directly is not exposed as a resource 
# in the Pulumi's Azure provider, and this would typically be done using Azure's ARM templates, 
# CLI, or directly through the Azure Portal.

# However, both services are now provisioned, and you can use Azure's documentation to link them.
# See: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-catalog-ml-assets

# It's a good practice to export the IDs and primary endpoints of resources.
pulumi.export('resource_group_id', resource_group.id)
pulumi.export('purview_account_id', purview_account.id)
pulumi.export('ml_workspace_id', ml_workspace.id)
```

This program defines the management of metadata for ML workloads using Azure Machine Learning and Azure Purview. We start by declaring a new resource group wherein all subsequent resources will belong. Then, we create a new Azure Purview account which will be responsible for handling data governance and cataloging in Azure. Following that, we define an Azure Machine Learning Workspace, which is a centralized place for managing ML artifacts like models and experiments.

It's important to note that the direct linking of Azure ML workspaces with Purview accounts is typically handled via ARM templates or through the Azure Portal and may not be directly supported through Pulumi's Azure provider at the time of writing this. You would link them by configuring the Purview account to scan the ML workspace and catalog its assets.

Finally, we export the resource group ID, Purview account ID, and ML workspace ID so you can easily reference them outside of Pulumi if necessary.

Remember that your specific metadata management and use case might require additional configuration or services, and this is just an example to get you started.