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Snowflake Tempo Command Reference

Resource Management​

Initialize Application Resources​

CALL management.create_resources();

Purpose: Initializes the application by loading required model weights and configurations Required Permissions: Warehouse, compute pool, and task management access

Static Detection​

Run Inference​

CALL static_detection.inference('your_service_name');

Parameters:

  • your_service_name: Name of the service to analyze (string) Purpose: Executes inference on specified service data

Deep Dive Analysis​

CALL inspect.deepdive(sequence_id);

Parameters:

  • sequence_id: Identifier of the sequence to analyze (string/integer) Purpose: Investigates specific sequences flagged as anomalies

Automated Detection​

Start Automated Inference​

CALL automated_detection.start_automated_inference(
    'source_table_name',
    slot_number
);

Parameters:

  • source_table_name: Fully qualified name of the source table (string)
  • slot_number: Reference slot number (integer) Notes:
  • Scheduled for 8:00am UTC daily
  • Stream names are automatically generated based on slot numbers:
    • Slot 1: stream_one_interactions
    • Slot 2: stream_two_interactions
    • Slot 3: stream_three_interactions

Manage Automated Inference​

CALL automated_detection.alter_automated_inference('stream_name', 'action');

Parameters:

  • stream_name: Name of the stream to manage (string)
  • action: One of the following (string):
    • 'suspend': Pause inference while maintaining stream updates
    • 'resume': Restart paused inference
    • 'stop': Terminate inference and clear source data

Example Usage:

-- Suspend stream
CALL automated_detection.alter_automated_inference('stream_one_interactions', 'suspend');

-- Resume stream
CALL automated_detection.alter_automated_inference('stream_one_interactions', 'resume');

-- Stop stream
CALL automated_detection.alter_automated_inference('stream_one_interactions', 'stop');

Model Optimization​

Fine-tune Model​

CALL model_optimization.tune_model('service_name');

Parameters:

  • service_name: Name of the service for model tuning (string) Purpose: Updates model based on tuning log data from the reference page

Notes​

  • All commands require appropriate permissions for warehouse, compute pool, and task management
  • The management console is accessible via the Streamlit dashboard
  • Reference data can be managed through the web-based management interface