Inference Logs

Verify the model has services with autocapture enabled:

from snowflake.ml.registry import Registry

session = <SESSION_SETUP>  # Per environment guide
reg = Registry(session=session, database_name="<DATABASE>", schema_name="<SCHEMA>")
mv = reg.get_model("<MODEL_NAME>").version("<VERSION>")

services_df = mv.list_services()
print(services_df[['name', 'status', 'autocapture_enabled']])

If no services have autocapture_enabled=True:

None of the services for this model have Auto-Capture enabled.

Auto-Capture must be enabled when creating the service (it cannot be added later).
To enable it, you would need to recreate the service with autocapture=True.

Would you like help creating a new service with Auto-Capture enabled?

If yes, load ../spcs-inference/SKILL.md.

Step 3: Collect Filter Options

Ask user:

You can filter the inference logs. All filters are optional — press Enter to skip any:

- Model version (e.g., V1):
- Service name (e.g., MY_SERVICE):
- Gateway name (if using a gateway):
- Time range: How far back? (e.g., "1 hour", "24 hours", "7 days")

⚠️ STOP: Wait for user response.

Step 4: Query Inference Data

Basic Query (All Logs)

SELECT *
FROM TABLE(INFERENCE_TABLE('<MODEL_NAME>'));

Filtered Query

Include only the filters the user provided:

SELECT *
FROM TABLE(
    INFERENCE_TABLE(
        '<MODEL_NAME>',
        VERSION => '<VERSION>',
        SERVICE => '<SERVICE_NAME>',
        GATEWAY => '<GATEWAY_NAME>'
    )
)
WHERE TIMESTAMP > DATEADD('<unit>', -<N>, CURRENT_TIMESTAMP());

Filter by Function Name

SELECT *
FROM TABLE(INFERENCE_TABLE('<MODEL_NAME>'))
WHERE RECORD_ATTRIBUTES:"snow.model_serving.function.name" = '<function_name>';

Sample Recent Logs

SELECT *
FROM TABLE(INFERENCE_TABLE('<MODEL_NAME>'))
WHERE TIMESTAMP > DATEADD('hour', -1, CURRENT_TIMESTAMP())
ORDER BY TIMESTAMP DESC
LIMIT 100;

Step 5: Present Results and Offer Analysis

After running the query, present the results and ask:

I found <N> inference records. What would you like to do?

1. View the raw data
2. Summarize request/response patterns
3. Analyze input feature distributions
4. Done

⚠️ STOP: Wait for user response.

Request Volume Over Time

SELECT
    DATE_TRUNC('hour', TIMESTAMP) as hour,
    COUNT(*) as request_count
FROM TABLE(INFERENCE_TABLE('<MODEL_NAME>'))
WHERE TIMESTAMP > DATEADD('day', -1, CURRENT_TIMESTAMP())
GROUP BY 1
ORDER BY 1;

Inference Table Data Schema

Important Notes

• Only successful requests are captured. Failed requests are not logged.
• Filter arguments must reference existing entities. If you recreated a service with the same name, queries only return data from the current service.
• Data is retained after deletion. Inference data persists even after deleting a service or version, as long as the model still exists.
• Deleting the model permanently deletes all inference data.
• 1MB limit per event. Data exceeding this is progressively truncated (strings shortened, then payload dropped).
• Performance tip: Always filter by TIMESTAMP for large inference tables.

Querying Historical Data for Deleted Entities

Even after deleting a service, version, or gateway, you can still query the historical data:

-- All logs for model (includes deleted services)
SELECT * FROM TABLE(INFERENCE_TABLE('<MODEL_NAME>'));

-- Filter by deleted version
SELECT * FROM TABLE(INFERENCE_TABLE('<MODEL_NAME>', MODEL_VERSION => '<VERSION>'));

-- Filter by deleted service
SELECT * FROM TABLE(INFERENCE_TABLE('<MODEL_NAME>', MODEL_VERSION => '<VERSION>', SERVICE => '<SERVICE>'));

Common Use Cases

Debugging Unexpected Predictions

SELECT
    RECORD_ATTRIBUTES:"snow.model_serving.request.data" as input,
    RECORD_ATTRIBUTES:"snow.model_serving.response.data" as output,
    TIMESTAMP
FROM TABLE(INFERENCE_TABLE('<MODEL_NAME>'))
WHERE TIMESTAMP > DATEADD('hour', -1, CURRENT_TIMESTAMP())
ORDER BY TIMESTAMP DESC
LIMIT 10;

Building a Retraining Dataset

CREATE TABLE training_data_from_prod AS
SELECT
    RECORD_ATTRIBUTES:"snow.model_serving.request.data.feature1"::FLOAT as feature1,
    RECORD_ATTRIBUTES:"snow.model_serving.request.data.feature2"::FLOAT as feature2,
    RECORD_ATTRIBUTES:"snow.model_serving.response.data.prediction"::FLOAT as prediction,
    TIMESTAMP
FROM TABLE(INFERENCE_TABLE('<MODEL_NAME>'))
WHERE TIMESTAMP > DATEADD('day', -30, CURRENT_TIMESTAMP());

Comparing Model Versions (A/B Testing)

SELECT
    RECORD_ATTRIBUTES:"snow.model_serving.version" as version,
    AVG(RECORD_ATTRIBUTES:"snow.model_serving.response.data.score"::FLOAT) as avg_score,
    COUNT(*) as request_count
FROM TABLE(INFERENCE_TABLE('<MODEL_NAME>'))
WHERE TIMESTAMP > DATEADD('day', -7, CURRENT_TIMESTAMP())
GROUP BY 1;