1. Event Sourcing for AI Models with Azure Cosmos DB


    Event sourcing is an architectural pattern where state changes are logged as a sequence of events. When applied to AI models, this can be particularly useful for tracking the decisions made by the model, the data that was input to the model when it made a decision, and so on. This can be valuable for auditing, debugging, and improving AI models over time.

    To implement event sourcing for AI models with Azure, you can use Azure Cosmos DB, a globally distributed, multi-model database service. Cosmos DB supports multiple data models, including documents, key-value, graph, and column-family. For event sourcing, the document model is often the most natural fit, as events can be stored as JSON documents.

    The following program will help to set up an event sourcing infrastructure for AI models using Azure Cosmos DB with Pulumi in Python:

    1. Azure Cosmos DB Account: This is the top-level resource in Cosmos DB which provides you with the ability to scale and replicate your data across multiple Azure regions.

    2. Azure Cosmos DB SQL Database: Within the Cosmos DB account, you will create an SQL (Document) API database which will store the JSON-formatted events.

    3. Azure Cosmos DB Container: Inside the SQL database, you will create a container to store and manage your events. Each event will be a JSON document within this container.

    This sample program will create a Cosmos DB account, a SQL database, and a container.

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('resource_group') # Create an Azure CosmosDB Account account = azure_native.documentdb.DatabaseAccount('account', resource_group_name=resource_group.name, location=resource_group.location, database_account_offer_type="Standard", locations=[{ "locationName": resource_group.location, "failoverPriority": 0, }], consistency_policy={ "defaultConsistencyLevel": "Session", }, # Enable multiple write locations to ensure the high availability of the data enable_multiple_write_locations=True, ) # Create a SQL API Cosmos DB Database database = azure_native.documentdb.SqlResourceSqlDatabase('database', resource_group_name=resource_group.name, account_name=account.name, resource={ "id": "eventSourcingDb", }, options={ "throughput": 400, }, ) # Create a Container for events in the database container = azure_native.documentdb.SqlResourceSqlContainer('container', resource_group_name=resource_group.name, account_name=account.name, database_name=database.name, resource={ "id": "eventsContainer", # Define the partition key for the container for better scalability "partitionKey": { "paths": ["/eventType"], "kind": "Hash", }, }, options={} ) # Export the Cosmos DB account endpoint and primary master key pulumi.export('cosmosdb_endpoint', account.document_endpoint) pulumi.export('primary_master_key', account.list_keys.apply(lambda keys: keys.primary_master_key))

    In the provided code, firstly, we create a resource group to hold all the related resources for Cosmos DB. Then, within that resource group, we create the Cosmos DB account with the "Standard" offer type and session consistency level for read operations, which is appropriate for many use cases within event sourcing.

    We then create a SQL (Document) Database called "eventSourcingDb". Within that database, we create a container named "eventsContainer". The partition key is specified as /eventType, which is appropriate for storing events of different types and querying them efficiently. It also enables the container to distribute the data across multiple physical partitions for scalability.

    At the end of the program, two important pieces of information are exported: the endpoint for the Cosmos DB account and the primary master key, which you can use to connect to the database from your application.

    As for working with Cosmos DB from an application, that will involve using the Azure Cosmos DB SDK to read and write data to the container according to your business logic. You would typically log events every time your AI model processes a new piece of data or makes a decision. The AI application will not be covered here, as it is an extensive topic on its own and would involve specifics of your AI model and operational logic.

    Remember to replace the string literals like "eventsContainer" and "eventSourcingDb" with your own identifiers as needed. This program assumes that the Pulumi CLI and Azure provider have been set up and appropriately configured in your environment to deploy resources.