Secure Data Sharing for AI Model Training

Question

Pulumi · Accepted Answer

To secure data sharing for AI model training, we can use several cloud services depending on the specific cloud provider. For Azure, Pulumi provides resources like `LinkedWorkspace`, `MachineLearningDataset`, and `ManagedNetworkSettingsRule` which are part of the Machine Learning Services that can be used to securely share datasets between workspaces, register datasets for training, and ensure secure communication within a machine learning workspace.

Below I will outline the general steps to set up secure data sharing for AI model training using Azure as an example:

1. Create an Azure Machine Learning workspace, which serves as the top-level resource for Azure Machine Learning.
2. Define a `LinkedWorkspace` to link separate machine learning workspaces for secure data sharing.
3. Register a `MachineLearningDataset` which represents the data source for the model training.
4. Implement `ManagedNetworkSettingsRule` to ensure secure communication within your machine learning workspace.

Let's write a Pulumi program that sets up these resources. The following Python program creates a machine learning workspace and configures the necessary resources for secure data sharing:

```python
import pulumi
import pulumi_azure_native as azure_native

# Define the resource group your resources will reside in
resource_group = azure_native.resources.ResourceGroup("resource_group")

# Create an Azure Machine Learning workspace
ml_workspace = azure_native.machinelearningservices.Workspace(
    "ml_workspace",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku=azure_native.machinelearningservices.SkuArgs(
        name="Standard"
    )
)

# Define a linked service workspace for secure data sharing, assuming you have another workspace
linked_service_workspace = azure_native.machinelearningservices.LinkedWorkspace(
    "linked_service_workspace",
    name="linkedServiceWorkspace",
    resource_group_name=resource_group.name,
    workspace_name=ml_workspace.name,
    properties=azure_native.machinelearningservices.LinkedWorkspacePropsArgs(
        linked_workspace_resource_id="/subscriptions/{subscription-id}/resourceGroups/{another-resource-group}/providers/Microsoft.MachineLearningServices/workspaces/{another-workspace-name}"
    )
)

# Register a machine learning dataset for model training
ml_dataset = azure_native.machinelearningservices.MachineLearningDataset(
    "ml_dataset",
    dataset_name="training_dataset",
    workspace_name=ml_workspace.name,
    resource_group_name=resource_group.name,
    dataset_type="Tabular",  # Assuming we are using tabular data
    parameters=azure_native.machinelearningservices.DatasetParamsArgs(
        # Specify the path and other data-specific parameters here
    )
)

# If required, define a managed network rule to secure your workspace communications
network_settings_rule = azure_native.machinelearningservices.ManagedNetworkSettingsRule(
    "network_settings_rule",
    rule_name="allowWorkspaceCommunication",
    workspace_name=ml_workspace.name,
    resource_group_name=resource_group.name,
    properties=azure_native.machinelearningservices.ManagedNetworkSettingsRulePropsArgs(
        # Set properties according to your security needs, such as the list of allowed public network access etc.
    )
)

# Export the important end-points or access points required to interact with the setup.
pulumi.export("workspace_url", ml_workspace.workspace_url)
pulumi.export("linked_service_workspace_id", linked_service_workspace.id)
pulumi.export("ml_dataset_id", ml_dataset.id)
```

In the above code:

- We're using the `azure_native.machinelearningservices` Pulumi package to create resources related to Azure Machine Learning.
- A `ResourceGroup` is created to scope the resources.
- An Azure Machine Learning workspace (`Workspace`) is created which is the fundamental building block where all machine learning assets are stored.
- We create a `LinkedWorkspace` which is essentially a pointer to another workspace where we can share data securely.
- A `MachineLearningDataset` is created and configured to be the data source for model training.
- The `ManagedNetworkSettingsRule` (if required) is a definition for managing network security for interactions with the machine learning workspace.

This setup is typical for a scenario where secure data sharing is a priority for AI model training in the cloud. Adjustments would need to be made based on the specifics of the data, security requirements, and other cloud services involved.