Model Audit

Audit pre and post model predictions and performance metrics to support model observability, explainability and drift management


This optional configuration provide the ability to audit predications to enable model retraining, feature and prediction drift management, model observability, and any local business governance requirements.

The configuration will dynamically create an in-memory database table, using the process name as the target table, and rest endpoints to enable direct access and export functions.

Use this feature to support external drift monitoring and model retraining


pmml predictor:
  name: irisScorer
  model: ./models/iris_rf.pmml
  response field: flowerPrediction
  audit configuration:
    target schema: ml_audit
    queue capacity: 5000
    flush frequency: 300


AttributeDescriptionData TypeRequired

target schema

name of the target schema where the table will be created


queue capacity

Number of prediction results to queue before flushing to target table

Integer Default: 1000

flush frequency

Frequency the queue will be flushed to table as seconds

Long Default: 60 Seconds


Contact Fractalworks for details

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