1. Versioning and Preservation of AI Training Data


    When working with AI and machine learning, versioning and preservation of training data are crucial for reproducibility, auditability, and continuous improvement of models. With Pulumi, you can define infrastructure for managing datasets with precise version control. Let's consider you are using Azure as your cloud provider. Azure Machine Learning offers various resources to help with this, such as DataVersion, Datastore, and DataContainer which allow you to organize, version, and manage access to your data.

    Here is how you can use Pulumi to define a setup for versioning and preserving AI training data in Azure:

    1. Datastore is used to set up a storage mechanism where you can maintain your training datasets.
    2. DataContainer acts as an entity to hold different versions of datasets.
    3. DataVersion represents a specific version of data in your container that you can reference in your machine learning experiments.

    Before you proceed with the Pulumi program, ensure you have the Azure CLI and Pulumi CLI installed and configured with the necessary Azure account and subscription.

    import pulumi import pulumi_azure_native.machinelearningservices as ml # Create a machine learning workspace ml_workspace = ml.Workspace( resource_name="myMlWorkspace", location="East US", # You should choose the location appropriate for you resource_group_name="myResourceGroup", # The resource group predefined in your Azure setup sku=ml.SkuArgs(name="Enterprise"), # You can customize the SKU as per your requirements ) # Create a datastore in the workspace ml_datastore = ml.Datastore( resource_name="myDatastore", workspace_name=ml_workspace.name, resource_group_name=ml_workspace.resource_group_name, # The properties of the datastore, such as underlying storage details, can be defined here # For this example, assume it uses the default blob storage associated with the workspace ) # Create a data container for versioning datasets ml_data_container = ml.DataContainer( resource_name="myDataContainer", workspace_name=ml_workspace.name, resource_group_name=ml_workspace.resource_group_name, properties={ "description": "Container for versioning training data" } ) # Create a data version, representing a specific version of dataset ml_data_version = ml.DataVersion( resource_name="myDataVersion", name="v1.0", # Name representing the version of the dataset workspace_name=ml_workspace.name, resource_group_name=ml_workspace.resource_group_name, container_name=ml_data_container.name, # DataVersion properties including the location of the actual data can be defined here # For this example, let's assume a path to the curated dataset is provided properties={"path": "path/to/the/training/dataset/v1.0"} ) # Use pulumi.export to output the names of the created resources pulumi.export("workspace_name", ml_workspace.name) pulumi.export("datastore_name", ml_datastore.name) pulumi.export("data_container_name", ml_data_container.name) pulumi.export("data_version_name", ml_data_version.name)

    This Pulumi program defines a workspace for machine learning experiments, a datastore for storing datasets, a container for holding dataset versions, and a specific data version for referencing in your experiments.

    Running this Pulumi program will provision the necessary Azure infrastructure to version and preserve your AI training data. As you progress with your projects, you can create additional versions by repeating the DataVersion resource definition with different names and paths. This will help you track changes over time, facilitate model comparisons, and maintain a reliable audit trail of how your models were trained.