1. Long-Term Retention of AI Training Data in Azure.


    To achieve long-term retention of AI training data in Azure, you'll want to create and manage resources to securely store, archive, and possibly back up your data. Azure Machine Learning datasets and Azure Blob Storage are two Azure services that can help you to organize and maintain your training data over time.

    Here’s a general approach to your requirement:

    1. Azure Blob Storage: You will use Azure Blob Storage to store your AI training data. Azure Blob Storage offers cost-effective, tiered storage and policies that can automate the transfer of data to appropriate storage tiers (hot, cool, or archive) based on data access patterns.

    2. Azure Machine Learning Datasets: With Azure Machine Learning, datasets provide a way to create, register, and maintain data objects that contain data your models need for training and inference. These datasets can help you manage your data in a structured way.

    3. Azure Backup and Archive Policies: Azure also provides backup services and policies that you can configure to ensure long-term retention of your data. Specifically, you might want to look into Azure Backup Long Term Retention Policies for databases if you're storing your data in an Azure SQL Database.

    4. Azure Security and Management Tools: To ensure the security and sustainability of your data storage ecosystem, use additional Azure tools like Azure Security Center and Azure Monitor.

    Now, let’s write a Pulumi program in Python that will set up a Blob Container in Azure Blob Storage where you can store AI training data. We'll also register a Machine Learning Dataset with Azure Machine Learning, designed for general structure and should be extended based on specific use cases.

    import pulumi import pulumi_azure_native as azure_native # Configuring Azure Resource Group resource_group = azure_native.resources.ResourceGroup('ai_data_resource_group') # Set up a storage account where the training data will be stored storage_account = azure_native.storage.StorageAccount('aistorageaccount', resource_group_name=resource_group.name, kind="StorageV2", sku=azure_native.storage.SkuArgs( name ="Standard_LRS" # Using Locally Redundant Storage for cost efficiency ) ) # Create a blob storage container within the storage account blob_container = azure_native.storage.BlobContainer('aiblobcontainer', account_name=storage_account.name, resource_group_name=resource_group.name, public_access="None" # Setting the public access to None for privacy ) # Azure Machine Learning Workspace creation ml_workspace = azure_native.machinelearningservices.Workspace('ai_ml_workspace', resource_group_name=resource_group.name, sku=azure_native.machinelearningservices.SkuArgs( name="Basic" # Choosing the pricing tier ), location=resource_group.location, description="Workspace for AI training data and model management" ) # Register a dataset with Azure Machine Learning ml_dataset = azure_native.machinelearningservices.MachineLearningDataset('ai_ml_dataset', resource_group_name=resource_group.name, workspace_name=ml_workspace.name, dataset_properties=azure_native.machinelearningservices.DatasetResourceDatasetPropertiesArgs( description="AI training data", dataset_type="Tabular", parameters=azure_native.machinelearningservices.DatasetResourceDatasetPropertiesArgsParametersArgs( datastore_name=blob_container.name, path=azure_native.machinelearningservices.DatasetResourceDatasetPropertiesArgsParametersArgsPathArgs( data_path="path/to/training/data" # Replace with actual path to your data ) ) ) ) # Exports pulumi.export('storage_account_name', storage_account.name) pulumi.export('blob_container_name', blob_container.name) pulumi.export('ml_workspace_name', ml_workspace.name) pulumi.export('ml_dataset_name', ml_dataset.name)

    This program sets up the necessary resources for storing and organizing your AI training data in Azure.

    • Resource Group: All resources are grouped under a single resource group for better management and separation of concerns.
    • Storage Account: A storage account is created to store large numbers of data objects in Azure Storage. Here, we have used the "Standard_LRS" SKU for cost efficiency. There are other SKUs available based on redundancy and performance requirements.
    • Blob Container: A specific blob container within the storage account is designated to store the AI training data blobs. The public access is set to None for privacy reasons.
    • Machine Learning Workspace: This is a prerequisite when working with Azure Machine Learning services. It provides a centralized place to work with all the Azure Machine Learning services.
    • Machine Learning Dataset: Datasets are ML-specific data abstracts that make it easier to manage your data. In this script, we set up a "Tabular" dataset structure which you can associate with your actual data.

    Make sure to replace 'path/to/training/data' with the actual path to your AI training data within the blob container.

    Remember to follow Azure's best practices for securing and managing your storage account and machine learning workspace. Azure permissions and network settings should be configured according to the security requirements of your project.