Skip to content

Model Training

UC-AIML-001: Retraining on New Data

Purpose: Improve model accuracy using recent tenant data.

Capabilities Breakdown:

1. Automated Retraining (UC 80.1)

  • Trigger: "Model Drift" detected (Accuracy < 85%) OR "New Data Volume" > 1000 records.

  • Pipeline: Extract Data -> Cleanse -> Train XGBoost -> Evaluate.

  • Auto-Promote: If New Model Accuracy > Old Model + 2%, promote to Staging.

2. Model Registry (UC 80.3)

  • Versioning: Track v1.0, v1.1 with metadata (Training Date, Dataset Hash).

  • Rollback: One-click revert to v1.0 if v1.1 shows bias in production.

3. Data Annotation (UC 80.4)

  • Feedback Loop: Front desk corrections ("This wasn't a Cancel, it was Reschedule") feed back into training set.

  • Human-in-the-Loop: Flag low-confidence predictions (< 60%) for human review.

Main Success Scenario:

  1. ML Pipeline detects "Churn Prediction" accuracy dropped to 82%.
  2. Pipeline triggers retraining on last 90 days data.
  3. New model achieves 88%.
  4. System deploys new model to staging endpoint.
  5. Engineer approves promotion to production.

Acceptance Criteria:

  1. [ ] Retraining triggers automatically upon drift detection.
  2. [ ] Previous 5 model versions are retained for rollback.
  3. [ ] Training data is anonymized before processing.