Integration of Azure Storage with AI Apps in Controlled Environments

Question

Pulumi · Accepted Answer

When integrating Azure Storage with AI applications in controlled environments, you are often looking at securely storing and handling data for your AI workloads while ensuring compliance with various operational policies and regulations.

Azure provides a comprehensive set of services that can be used to build AI applications that leverage Azure Storage as the backend for storing data. These services include Azure Blob Storage for storing large amounts of unstructured data, Azure Queue for reliable messaging between application components, and different AI services like Azure Machine Learning for training models or Azure Cognitive Services for adding AI capabilities to your applications.

In this context, "controlled environments" might refer to environments with specific security and compliance requirements. This often involves configuring the storage and related services to comply with policies, encrypt data, and manage access control meticulously.

To accomplish this with Pulumi, which enables Infrastructure as Code (IaC), you will programmatically define your cloud infrastructure using Python. Below is a high-level explanation of the Pulumi program that will perform the integration of Azure Storage with AI Apps in a controlled environment:

1. **Set up Azure Resource Group**: Define a resource group to organize all the resources you'll create.
2. **Create Azure Storage Account**: Define a storage account where blobs, files, queues, and tables will be stored.
3. **Create Blob Containers**: Set up containers within the Blob Storage to organize your blobs.
4. **Configure Storage Account Network Rules**: If your environment is controlled, you may need to set up network rules to limit access to the storage account.
5. **Create AI resources**: Set up resources like an Azure Machine Learning workspace.
6. **Integrate Storage with AI**: Use the storage account information to store and retrieve data from your AI applications.

Let's write a Pulumi program that sets up an Azure Storage Account and integrates it with Azure Machine Learning in a controlled environment. We'll configure the storage to be private and accessible only from within Azure services or specific IP ranges, representing a typical controlled environment setting.

```python
import pulumi
import pulumi_azure_native as azure_native

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

# Create an Azure Storage Account
storage_account = azure_native.storage.StorageAccount("aistorageaccount",
    resource_group_name=resource_group.name,
    sku=azure_native.storage.SkuArgs(
        name=azure_native.storage.SkuName.STANDARD_LRS,
    ),
    kind=azure_native.storage.Kind.STORAGE_V2,
    network_rule_set=azure_native.storage.NetworkRuleSetArgs(
        default_action=azure_native.storage.DefaultAction.DENY,
        ip_rules=[
            azure_native.storage.IPRuleArgs(
                action='Allow',
                ip_address_or_range='203.0.113.0/24',
            ),
        ],
    ),
    location=resource_group.location,
)

# Create a Blob Container
blob_container = azure_native.storage.BlobContainer("aiblobcontainer",
    account_name=storage_account.name,
    resource_group_name=resource_group.name,
    public_access=azure_native.storage.PublicAccess.NONE,  # Disallow public access
)

# Create an Azure Machine Learning Workspace
ml_workspace = azure_native.machinelearningservices.Workspace("aiworkspace",
    resource_group_name=resource_group.name,
    sku=azure_native.machinelearningservices.SkuArgs(
        name="Basic",
    ),
    location=resource_group.location,
    storage_account=storage_account.id,  # Associate storage with ML workspace
)

# Output the primary storage account key
primary_storage_key = pulumi.Output.secret(storage_account.primary_access_key)

# Export the IDs of the resources to be used in other Pulumi stacks or referenced externally
pulumi.export('resource_group_id', resource_group.id)
pulumi.export('storage_account_id', storage_account.id)
pulumi.export('blob_container_id', blob_container.id)
pulumi.export('ml_workspace_id', ml_workspace.id)
pulumi.export('primary_storage_key', primary_storage_key)
```

In this program:
- We start by importing Pulumi's Python package, specifically, the Azure Native provider, which allows us to work with Azure resources.
- A new resource group is created to hold all resources related to the AI application.
- We then create the Azure Storage Account with specific Storage Kind and SKU (Stock Keeping Unit), which specifies the performance tier.
- Network rules are set to DENY by default, but an IP rule is added to allow access to the Storage Account from a specified IP range. This represents a "controlled environment" where access is limited.
- A Blob Container is added to the Storage Account with public access set to NONE to ensure privacy and control.
- An Azure Machine Learning Workspace is created and associated with our Storage Account, allowing AI Apps run within this workspace to use the storage.
- The primary key of the Storage Account is outputted using `Output.secret()` which masks the value when displaying it in Pulumi interfaces.

Finally, we export important IDs and the primary access key (masked as a secret) using `pulumi.export`, which allows them to be used as inputs to other stacks or for external reference.