Train or retrain a credit scoring model. Use when the user asks to train, retrain, fine-tune, or calibrate a model, or when new training data is available.
Train or retrain an OpenCredit scoring model with full MLflow tracking and post-training validation.
data/ or feature store)configs/models/uv run mlflow ui or docker service)uv run python -m opencredit.models.train \
--config configs/models/<model_type>.yaml \
--experiment-name <descriptive_name> \
--tags market=<market> version=<semver>
After training completes, immediately run evaluation:
uv run python -m opencredit.models.evaluate \
--model-id <mlflow_run_id> \
--test-data data/test.parquet
Check these metrics meet thresholds:
uv run python -m opencredit.compliance.bias_audit \
--model-id <mlflow_run_id> \
--attributes gender age_group region
Fail criteria: disparate impact ratio outside 0.8-1.25 on ANY group.
uv run python -m opencredit.compliance.docs_generator \
--model-id <mlflow_run_id> \
--output docs/compliance/
Only if evaluation AND bias audit pass:
uv run python -m opencredit.models.register \
--model-id <mlflow_run_id> \
--stage production