1. Analyzing AI Model Performance Metrics with Databricks SQL Queries


    To analyze AI model performance metrics using Pulumi with Databricks, we will leverage Databricks SQL queries. Databricks offers an integrated platform for data science, data engineering, and analysis, making it an ideal choice for such tasks.

    We'll create a Databricks SQL query object that can be executed on a schedule or on-demand to retrieve model performance metrics. Databricks allows you to define SQL queries that can analyze data within the Databricks ecosystem—using data already available in your tables or structured in a way that Databricks can interpret.

    To get started, let's first install the pulumi_databricks package to interact with Databricks resources within our Pulumi program:

    pip install pulumi_databricks

    The key resource we'll be using in our Pulumi program is the SqlQuery object from the pulumi_databricks module, which represents a Databricks SQL Query. The SqlQuery resource allows you to create, manage, and execute SQL queries in Databricks.

    In the following Python program, we are going to define a SQL query to extract AI model performance metrics. Let's assume we have already trained models and logged metrics to a table in Databricks and now we want to analyze them with SQL. Our program will set up a regular analysis query that runs daily.

    Here is how you can set up such a Pulumi program:

    import pulumi import pulumi_databricks as databricks # Define a SQL Query to analyze AI model performance metrics model_perf_query = databricks.SqlQuery("model-performance-query", datasource_id="the-datasource-id", # Replace with your actual data source ID in Databricks name="AnalyzeAIPerformance", description="Query to analyze AI model performance", query=""" SELECT model_name, AVG(accuracy) as avg_accuracy, AVG(f1_score) as avg_f1_score FROM model_metrics GROUP BY model_name; """, schedule=databricks.SqlQueryScheduleArgs( # Define the schedule for the query daily=databricks.SqlQueryScheduleDailyArgs( time_of_day="06:00", interval_days=1 ) ) ) # Export the ID of the query to be used elsewhere if necessary pulumi.export('query_id', model_perf_query.id)

    In the above code, replace the-datasource-id with the ID of the data source in Databricks where your model performance metrics are stored.

    The SqlQuery resource we declared(named model-performance-query) will execute a SQL query which averages the accuracy and f1_score of models and groups them by model_name from the model_metrics table. The declared schedule runs the query every day at 06:00 hours.

    You will also want to replace the query parameter's placeholder SQL with the actual SQL that correlates with your data scheme and analysis needs.

    Lastly, the pulumi.export('query_id', model_perf_query.id) line exports the ID of the created SQL query allowing you to use this ID in other parts of your system if needed, such as in monitoring processes or more complex workflows.

    After running pulumi up, this code will provision the query resource in Databricks. You will interact with the query through the Databricks UI, CLI, or APIs—wherever you access the rest of your Databricks resources.

    Please ensure that you have the appropriate Databricks service provider configured for Pulumi to interact correctly with your Databricks environment.