1. Storing Large Datasets for AI in Azure Blob Storage


    Storing large datasets for AI (Artificial Intelligence) applications often requires a robust and scalable storage solution. Azure Blob Storage is a service provided by Microsoft's Azure that offers scalable, durable cloud storage for unstructured data such as text or binary data, which is ideal for storing large datasets needed for AI applications.

    In the following Pulumi program written in Python, we're going to provision a storage account and a container within Azure Blob Storage where you can upload your large datasets. I'll guide you through the entire process.

    First, we import the pulumi_azure_native package to gain access to the Azure Native provider resources. This package provides the classes and methods that will allow us to interact with Azure services.

    Then, we create two primary resources:

    • An instance of StorageAccount, which represents an Azure storage account. A storage account provides a unique namespace to store and access your Azure storage data objects.
    • An instance of BlobContainer, which represents a container within the storage account. Containers serve as a way to organize sets of blobs within your storage account.

    Here's how we might write a program to accomplish this:

    import pulumi import pulumi_azure_native.storage as storage import pulumi_azure_native.resources as resources # Create an Azure Resource Group resource_group = resources.ResourceGroup("ai_dataset_resource_group") # Create an Azure Storage Account storage_account = storage.StorageAccount("ai_storage_account", resource_group_name=resource_group.name, location=resource_group.location, sku=storage.SkuArgs(name=storage.SkuName.STANDARD_LRS), kind=storage.Kind.STORAGE_V2 ) # Create an Azure Blob Storage Container blob_container = storage.BlobContainer("ai_blob_container", resource_group_name=resource_group.name, account_name=storage_account.name, public_access=storage.PublicAccess.NONE ) # Export the connection string for the storage account, and the primary blob endpoint primary_blob_endpoint = pulumi.Output.concat("https://", storage_account.name, ".blob.core.windows.net/") pulumi.export("connection_string", storage_account.primary_connection_string) pulumi.export("primary_blob_endpoint", primary_blob_endpoint)

    In the example above:

    • We create a resource group named ai_dataset_resource_group which acts as a logical container for the storage account and any other resources you may want to group together.

    • The storage account ai_storage_account is created in the STANDARD_LRS (Locally-redundant storage) performance tier. This tier is generally a cost-effective option for storing data that is not accessed frequently. Depending on your needs, you might choose a different tier.

    • The ai_blob_container is our blob container where you'll store your datasets. It is created with public_access set to None, which means the data within it is private and not accessible over the internet unless you explicitly set permissions.

    • Finally, we output the connection string and the primary blob endpoint for the storage account. The connection string is important as it allows you to authenticate access to the storage account programmatically from your AI applications or tools.

    When you run this program with Pulumi, it will provision these resources in Azure, and you'll have a place to start uploading and managing your AI datasets. Remember that you'll need to have the Azure CLI installed and be logged in with an account that has permissions to create resources in your Azure subscription.