1. Storing Streaming Data for Real-time AI Inferencing on Azure Blob


    When dealing with streaming data and real-time AI inferencing, we need a foundation to capture, store, and process the data before sending it to models for inferencing. In Azure, this is commonly achieved by using a set of services:

    1. Azure Event Hubs or Kafka: These services act as the entry point for the streaming data, capturing data in real-time from various sources.
    2. Azure Stream Analytics: This service can be used to process streaming data in real-time.
    3. Azure Blob Storage: Blob storage will be the destination where processed data streams are stored and can be accessed by AI models for inferencing.
    4. Azure Machine Learning: To create, train, and deploy AI models that can make real-time predictions based on the streaming data.

    Below is a basic Pulumi program written in Python that sets up Azure Blob Storage for storing streaming data. By focusing on the Blob Storage component, this example assumes that the streaming sources and real-time data processing have already been set up.

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('ai_inferencing_resource_group') # Create an Azure Storage Account storage_account = azure_native.storage.StorageAccount('aiinferencestorage', 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 ) # Create an Azure Blob Storage Container within the Storage Account blob_container = azure_native.storage.BlobContainer('aiinferenceblobcontainer', account_name=storage_account.name, resource_group_name=resource_group.name, public_access=azure_native.storage.PublicAccess.NONE ) # Create an Azure Blob within the Blob Container # This would be a placeholder blob, and in an actual streaming setup, # streaming data would be directed here by stream processing services like Azure Stream Analytics blob = azure_native.storage.Blob('aiblob', account_name=storage_account.name, container_name=blob_container.name, resource_group_name=resource_group.name, # Blob properties can be set depending on the requirements properties=azure_native.storage.BlobPropertiesArgs( blob_type=azure_native.storage.BlobType.BLOCK_BLOB, ) ) # Export the URLs needed to access the Blob Storage pulumi.export('resource_group_name', resource_group.name) pulumi.export('storage_account_name', storage_account.name) pulumi.export('blob_container_name', blob_container.name) pulumi.export('blob_url', pulumi.Output.concat( 'https://', storage_account.name, '.blob.core.windows.net/', blob_container.name, '/', blob.name ))


    • We first create an Azure Resource Group, which is a container that holds related resources for an Azure solution.

    • Next, we create an Azure Storage Account with STANDARD_LRS (Locally-Redundant Storage) SKU, which is a cost-effective storage option offering high durability.

    • We then define a Blob Container inside the Storage Account. This will be the container to store our blobs (files), which in the context of streaming and AI, would be processed streaming data files.

    • A single Blob item is created as a depiction of storing streaming data. Typically, blobs would be created and managed automatically by a stream processing service like Azure Stream Analytics as data streams in. Here, we set the type as BLOCK_BLOB, which is suitable for text or binary data, including documents and media files.

    • Finally, we export the details such as Resource Group Name, Storage Account Name, Blob Container Name, and the URL to access the blob. Accessing the blob might require additional authentication setup based on how you plan to use it for AI inferencing.

    In practice, you would also set up services to process the incoming streaming data (e.g., Azure Stream Analytics jobs) and make sure that processed data is directed to this Blob storage for AI inferencing. Then, you would incorporate Azure Machine Learning to apply trained models for real-time inferencing on the data stored in the blob.