1. Storing and Accessing Data for ML with Azure Blob Storage


    To store and access data for machine learning with Azure Blob Storage, we will follow the following steps:

    1. Create a Resource Group: A Resource Group in Azure is a logical container into which Azure resources like web apps, databases, and storage accounts are deployed and managed.

    2. Create a Storage Account: This is a unique namespace in Azure for your data, where blob containers are stored. A storage account can hold large amounts of unstructured data, such as text or binary data, which is ideal for ML data storage.

    3. Create a Blob Container: Containers act as directories for blobs, similar to a folder in a file system. They are used to store and organize your blobs within the storage account.

    4. Upload and Access Blobs: Once the containers are ready, you can upload your data as blobs into them. Blobs can be any type of text or binary data, like documents or media files. In the context of ML, this is where you would store datasets and model artifacts.

    5. Manage Access: We need to ensure the data's security by setting access policies for our blobs and containers.

    Below is a Pulumi program written in Python which sets up Azure Blob Storage and allows you to store and access your machine learning data.

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('resource_group') # Create an Azure Storage Account storage_account = azure_native.storage.StorageAccount('storage_account', # Arguments for storage account creation resource_group_name=resource_group.name, sku=azure_native.storage.SkuArgs( name=azure_native.storage.SkuName.STANDARD_LRS, # LRS = Locally-redundant storage ), kind=azure_native.storage.Kind.STORAGE_V2 ) # Create a Blob Container within the Storage Account blob_container = azure_native.storage.BlobContainer("blob_container", account_name=storage_account.name, resource_group_name=resource_group.name, # Public access level can vary depending on your security requirements public_access=azure_native.storage.PublicAccess.NONE, ) # Export the details about the Storage Account and Container for access pulumi.export('resource_group_name', resource_group.name) pulumi.export('storage_account_name', storage_account.name) pulumi.export('storage_account_primary_connection_string', storage_account.primary_connection_string) pulumi.export('blob_container_name', blob_container.name)

    In this program:

    • We first import the necessary Pulumi packages.
    • We create an Azure resource group to contain all of our resources.
    • A storage account is then created with a standard locally-redundant storage (LRS) replication strategy for durability.
    • Inside the storage account, we create a blob container with no public access by default to ensure that our ML data remains secure.
    • At the end, we export the names and the primary connection string of the storage account for later access, possibly for a CI/CD pipeline or an application.

    What's particularly important in machine learning scenarios is the ability to programmatically access stored data, so keep the primary connection string secure as it provides programmatic access to your storage account.

    Once you have set up the blob storage, you can use Azure SDKs for Python, or tools like Azure Storage Explorer, to upload your ML data (datasets, training models, etc.) to the Blob Storage.

    Remember to maintain proper security practices by managing access keys, connection strings, and access permissions diligently, especially when working with sensitive ML data.