Model audit

Support explainability, detect drift, and manage retraining through model auditing

Objective

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

Example

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

Attributes schema

AttributeDescriptionData TypeRequired

target schema

name of the target schema where the table will be created

String

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

Support

For more details or assistance, Fractalworks is ready to support in your enquiries.

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