1. Time-Series Data Storage for AI-model Performance Tracking.


    To create a time-series database for tracking AI model performance, we can use AWS Timestream, which is a fast, scalable, and serverless time-series database service built for IoT and operational applications. Timestream makes it easy to store and analyze trillions of events per day at 1/10th the cost of relational databases and provides automated rollups, retention, tiered storage, and querying of data.

    In this Pulumi program, we'll create a Timestream database and a Timestream table to store AI model performance metrics. Metrics might include things like prediction accuracy, inference time, model usage statistics, and more.

    Here are the steps this program will follow:

    1. Create a Timestream database: A logical grouping of tables in Timestream.
    2. Create a Timestream table: Where the actual time-series data will be stored within the database.
    3. Set the retention policies: Timestream tables support defining retention policies for both memory store (hot storage for recent data) and magnetic store (cold storage for historical data).

    Let's proceed with the Pulumi program written in Python:

    import pulumi import pulumi_aws as aws # Create a Timestream database for AI model performance metrics ai_model_perf_db = aws.timestreamwrite.Database("aiModelPerformanceDb") # Create a Timestream table within the database # Define retention policies for both recent and older data. # Timestream automatically moves the data to the appropriate storage based on the retention period. ai_model_perf_table = aws.timestreamwrite.Table("aiModelPerformanceTable", database_name=ai_model_perf_db.name, retention_properties={ "memory_store_retention_period_in_hours": 24, # Store recent data in memory for fast access for 1 day "magnetic_store_retention_period_in_days": 365, # Store historical data on magnetic storage for 1 year } ) # Export the table name and database name to be accessed outside of Pulumi if necessary pulumi.export('aiModelPerformanceDbName', ai_model_perf_db.name) pulumi.export('aiModelPerformanceTableName', ai_model_perf_table.name)

    Each record you insert into this Timestream table should include a timestamp and one or more measures. For AI model performance, the measures could be numerical metrics that you track over time. You would typically write to this table every time a model is used for inference or regularly during training to keep tabs on the model's performance.

    As your model is used, you can insert records into this table, which will automatically be moved to the appropriate storage tier based on the retention periods we've set. You can then use Timestream's query functionality to analyze the performance trends of your AI models over time.

    Remember, this program assumes you've already set up AWS credentials and the Pulumi CLI on your system, and have chosen aws as your cloud provider when initially creating your Pulumi project.