Backtest Monitoring

Mis à jour le 2025-12-22Confiance : high

backtest-monitoringevaluation-frameworkproduction-systemsregression-testingfield-level-analysisclassification-diffextraction-diffcriticality-weightspipeline-validationground-truth-comparison

Production evaluation system that re-runs document processing pipelines on reference datasets with verified ground truth to measure the impact of changes before deployment. Essential safety mechanism implemented at alan-health to prevent silent regressions across document categories.

Core Functionality

Pipeline Re-execution: Complete processing workflow from document input through transcription, classification, and extraction steps on curated reference datasets.

Ground Truth Comparison: Field-by-field comparison between system output and verified expected results across entire extraction schema.

Change Impact Measurement: Quantifies improvements and regressions before changes reach production environment.

Monitoring Components

Classification Diff Analysis:

Document category prediction accuracy
Sub-class classification performance
Category-specific error patterns

Extraction Diff Analysis:

Field-level accuracy comparison
Value-by-value extraction validation
Schema compliance measurement

Criticality Weighting System:

High priority: Financial amounts, critical dates, regulatory fields
Medium priority: Names, addresses, secondary identifiers
Low priority: Formatting variations, optional fields

Dashboard Integration

Visual Monitoring: Real-time tracking of accuracy metrics across document categories with regression alerts and improvement validation.

Aggregate Analysis: Results analyzed per category, per field, or in aggregate to identify patterns and guide development decisions.

Historical Tracking: Longitudinal performance monitoring enabling trend analysis and regression root cause identification.

Production Implementation

Pre-Deployment Validation: Every system change validated against reference datasets before production release.

Automated Safety Net: Prevents deployment of changes that degrade performance on any document category.

Development Confidence: Teams iterate with quantified impact visibility rather than subjective assessment.

Reference Dataset Requirements

Production Authenticity: Real documents from production pipeline with verified manual extractions as ground truth.

Representative Coverage: Spans all document types, quality levels, and edge cases encountered in production environment.