Teradata PMML Model Scoring

Core Capabilities

• Automated table structure analysis via DBC.ColumnsV
• Dynamic SQL generation for PMMLPredict
• Complete workflow from data preparation to results interpretation
• Data quality validation and preprocessing guidance
• Parameter optimization and tuning

Key Parameters

• InputTable: Data to score
• ModelTable: PMML model stored as CLOB (model_id, model columns)
• Accumulate: Columns to pass through
• Overwrite: 'true'/'false' for output column handling

Use Cases

1. Score data with PMML-exported models
2. Deploy R/SAS/SPSS models in-database
3. Standardized model interchange
4. Legacy model deployment

Example Usage

-- PMMLPredict execution
SELECT * FROM PMMLPredict (
    ON {USER_DATABASE}.{USER_TABLE} AS InputTable
    ON (SELECT model_id, model FROM {USER_DATABASE}.{MODEL_TABLE}
        WHERE model_id = '{MODEL_NAME}') AS ModelTable DIMENSION
    USING
    Accumulate ('{ID_COLUMN}', '{OTHER_COLUMNS}')
    Overwrite ('true')
) AS dt;

Scripts Included

Core Analytics Scripts

• table_analysis.sql: Automatic table structure discovery
• preprocessing.sql: Data preparation and feature engineering
• model_training.sql: PMMLPredict execution
• evaluation.sql: Results analysis and metrics
• complete_workflow_template.sql: End-to-end workflow

Utility Scripts

• data_quality_checks.sql: Comprehensive data validation
• parameter_tuning.sql: Parameter optimization
• diagnostic_queries.sql: Results diagnostics and interpretation

Best Practices

• Always run table_analysis.sql first to understand your data structure
• Validate data quality before executing the analytical function
• Use parameter_tuning.sql to find optimal configuration
• Review diagnostic_queries.sql output for model/results validation

Limitations

• Requires Teradata Vantage 17.20+ with ClearScape Analytics
• Input data must meet function-specific requirements
• Results depend on data quality and parameter configuration

Teradata PMML Model Scoring - ClearScape Analytics skill for Teradata Vantage