1. User Personalization with AI on Azure Cosmos DB


    To achieve user personalization with AI on Azure Cosmos DB, you will need to create instances of Azure Cosmos DB and provision the necessary resources for data storage and intelligence. Azure Cosmos DB is a globally distributed, multi-model database service that offers incredible throughput and latency guarantees. It is designed to handle large volumes of data and allows you to process and analyze this data in real-time to provide a personalized experience for users.

    Here’s a step-by-step Pulumi program written in Python that will help you provision an instance of Azure Cosmos DB, set up a database, and configure it for user personalization with AI:

    1. Cosmos DB Account: Begin by creating a Cosmos DB account that will act as the container for your databases, collections, and user data.
    2. Database: Within the Cosmos DB account, create a new database where user data will be stored and accessed.
    3. AI and Personalization: For user personalization, you will typically need to build or integrate AI models. Cosmos DB itself doesn't provide the AI functionality directly; instead, you would use Azure ML or Cognitive Services alongside Cosmos DB. However, setting up these services is beyond the immediate scope of Pulumi’s IaC capabilities and would typically involve data scientists and machine learning engineers who develop, train, and deploy models.

    Below is the Pulumi program for setting up a Cosmos DB account and creating a database for user personalization. This example assumes you have already installed Pulumi and set up the Azure provider.

    import pulumi import pulumi_azure_native as azure_native # Create a new resource group for the Cosmos DB account resource_group = azure_native.resources.ResourceGroup("resourceGroup") # Create an Azure Cosmos DB account account = azure_native.documentdb.DatabaseAccount("account", resource_group_name=resource_group.name, location=resource_group.location, database_account_offer_type="Standard", # Other types available based on your requirements # These properties enable multi-region writes and set the default consistency level locations=[{ "location_name": resource_group.location, "failover_priority": 0, "is_zone_redundant": False, }], # Enable geo-redundancy for high availability across regions enable_multiple_write_locations=True, # Set consistency policy for data replication and usage consistency_policy={ "default_consistency_level": "Session", "max_staleness_prefix": 100, "max_interval_in_seconds": 5 }) # Create a new Cosmos DB SQL Database (note: other APIs are also available for Cosmos DB, such as MongoDB, Cassandra, etc.) database_name = "userPersonalizationDB" sql_database = azure_native.documentdb.SqlDatabase("sqlDatabase", resource_group_name=resource_group.name, account_name=account.name, database_name=database_name, options={"throughput": 400}) # Set throughput according to your need (measured in RU/s) # Output the connection string for the Cosmos DB account, which will be used by your applications primary_connection_string = pulumi.Output.all(resource_group.name, account.name).apply( lambda args: azure_native.documentdb.list_database_account_connection_strings( resource_group_name=args[0], account_name=args[1])).apply(lambda result: result.connection_strings[0].connection_string) pulumi.export("cosmosDBConnectionString", primary_connection_string)

    This program follows these steps:

    • It imports the required pulumi and pulumi_azure_native modules.
    • Creates a new resource group in Azure to host our Cosmos DB account by instantiating ResourceGroup.
    • Defines a DatabaseAccount instance representing the Cosmos DB account, where data will be stored. This includes setting up locations, enabling multiple write locations, and defining a consistency policy that fits your user personalization needs.
    • Within this account, it creates a SqlDatabase that will store and manage the user data.
    • Finally, it exports the connection string for the created Cosmos DB account, which your applications can use to interact with the database to store and retrieve user data.

    Remember, personalization with AI involves more than just a database. You will also need to setup Azure Machine Learning or Cognitive Services to build and deploy AI models that will process the data in Cosmos DB to perform user personalization. The exact details depend on your specific application requirements and are typically handled by adding additional code, tools, and services on top of the infrastructure provisioned by Pulumi.