1. Relational Data Storage for AI Model Training on Azure SQL


    To set up a relational data storage system on Azure SQL for AI model training, you would typically create an Azure SQL Database or a Managed Instance, depending on your needs. Azure SQL Database is a fully managed platform as a service (PaaS) database engine that handles most of the database management functions such as upgrading, patching, backups, and monitoring without user involvement. Azure SQL Managed Instance is an expansion of the existing SQL Server database engine instance, with the full SQL Server feature-set and operating model, but hosted as a PaaS.

    In this case, we'll create an Azure SQL Database since it's a common choice for scenarios like AI model training where you need a managed relational database service with capabilities like high availability, security features, and scalability.

    Below is the Pulumi program that provisions a basic Azure SQL Database. Be sure to have Azure credentials configured for Pulumi as outlined in the Pulumi Azure setup page.

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('ai_model_rg') # Create an Azure SQL Server to host the database sql_server = azure_native.sql.Server("aiModelSqlServer", resource_group_name=resource_group.name, location=resource_group.location, version="12.0", # Set the version of the SQL Server instance. administrator_login='sqladmin', administrator_login_password='your-password-here', # Replace with a secure password. public_network_access="Enabled" ) # Create an Azure SQL Database within the SQL Server sql_database = azure_native.sql.Database("aiModelSqlDatabase", resource_group_name=resource_group.name, server_name=sql_server.name, location=resource_group.location, sku=azure_native.sql.SkuArgs( name="S0", tier="Standard" ), collation="SQL_Latin1_General_CP1_CI_AS" ) # Output the connection string for the database (without exposing password), adapt as necessary for your AI environment. sql_connection_string = pulumi.Output.all(sql_server.name, sql_database.name).apply( lambda args: f"Server=tcp:{args[0]}.database.windows.net;Database={args[1]};User ID=sqladmin;Encrypt=true;Connection Timeout=30;" ) pulumi.export('sql_connection_string', sql_connection_string)


    In the program above, we perform the following steps:

    1. Resource Group: Create an Azure Resource Group that will contain all of our Azure resources for organization and management purposes.

    2. SQL Server: Create an Azure SQL Server resource. This server will host the databases you need. We specify the server version, an administrator login, and a password. Always ensure you use a secure password here, and it's typically advised to use a secret manager or environment variables rather than hard-coding it into your Pulumi programs.

    3. SQL Database: Provision an Azure SQL Database within the SQL Server we've created. The sku parameter defines the performance characteristics of the database; "S0" is a starting point, and you should size this according to your workload requirements. The collation parameter is set to a typical SQL Server collation.

    4. Connection String: We build a connection string that can be used by your applications or AI models to connect to the database. Note that the password is not included in the connection string and should be handled securely.

    Execute the program by running pulumi up in the directory where this file is saved. It will provision the resources as per the plan shown by Pulumi.

    Remember to replace 'your-password-here' with your actual secure SQL admin password. It would be best to manage such sensitive data outside of your source code, possibly using Pulumi's config secrets or a managed identity with Azure Key Vault.