model-retrain

Automate ML model retraining by orchestrating data validation, training, and evaluation pipelines with enforced quality gates. This process controls promotions through staging transitions, reducing manual intervention and deployment risk.

Drift-driven retraining triggers automated workflows from data drift alerts. It executes training, data validation, and artifact logging, then requires human-in-the-loop approvals via PRs or MLflow UI before production rollout.

Yes, MLflow supports model promotion and governance approvals. The workflow enforces primary and secondary metrics, logs artifacts, and manages controlled transitions through staging and production environments.

Automated retraining supports scheduled, manual, and drift-driven triggers. It orchestrates the end-to-end workflow from data validation to production rollout, ensuring safe promotion policies are enforced across all scenarios.

Enforce safe model promotion policies by utilizing staging gates, fairness checks, and GitHub PR workflows. This approach ensures controlled transitions and governance approvals before any model reaches production.