Analyzing AI Model Performance Metrics with Databricks SQL Queries

Question

Pulumi · Accepted Answer

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:

```bash
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:

```python
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.