1. Relational Database Backing for Machine Learning Platforms on Azure.


    To create a relational database on Azure for a machine learning platform, you would typically use Azure SQL Database, which is a managed, scalable, and intelligent SQL database service. Leveraging Azure's native SQL database allows you to focus on your data and its analysis rather than the maintenance and management of the database itself.

    For machine learning workloads, having a SQL database is crucial as it allows you to store structured data efficiently, which is often required for machine learning algorithms to operate effectively. Furthermore, Azure SQL databases can be integrated with Azure Machine Learning services and other Azure services to enrich your machine learning pipelines, allowing you to combine data analysis, model training, and deployment into a cohesive workflow.

    Here's a Pulumi program written in Python that creates an Azure SQL Database. It includes the steps to set up an Azure resource group, an SQL server, and an SQL database. The program assumes you have already configured your Pulumi environment with the appropriate Azure credentials.

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('ml-sql-rg') # Create an Azure SQL Server that will host the SQL Database sql_server = azure_native.sql.Server( 'ml-sql-server', resource_group_name=resource_group.name, location=resource_group.location, administrator_login='adminuser', administrator_login_password='securePASSWORD123', # Change to a secure password version='12.0') # You can specify the version of SQL Server # Create an Azure SQL Database within the created SQL server sql_database = azure_native.sql.Database( 'ml-sql-database', resource_group_name=resource_group.name, server_name=sql_server.name, location=resource_group.location, sku=azure_native.sql.SkuArgs( name='S0', # You can choose different performance tiers based on your needs tier='Standard', capacity=10, # DTUs (Database Transaction Units) ), max_size_bytes=268435456000) # 250 GB, you can adjust the size as needed # Export the SQL database's URL so that it can be used to connect from other services sql_server_url = pulumi.Output.concat('jdbc:sqlserver://', sql_server.name, '.database.windows.net:1433;database=', sql_database.name) pulumi.export('sql_server_url', sql_server_url)

    In this program:

    • We define a resource group to contain all related resources for this infrastructure, which helps in organization and management.
    • We create an Azure SQL Server, which is a necessary component to manage databases.
    • We then create an Azure SQL Database hosted on the SQL Server. The database can be used to store and query data.
    • We export the connection URL for the SQL Database, which you can use in your machine learning application to connect to the database.

    Don't forget to replace administrator_login_password with a robust password specific to your requirements. Also, make sure to select an appropriate SKU (size and tier) for the database; here, we're using the 'S0' tier with a capacity of 10 DTUs, which might be fine for development or test workloads but likely not sufficient for production machine learning workloads.

    Please check the Azure SQL Database and Azure SQL Server documentation for more information on the configurable properties and pricing tiers.