ML CI/CD Security & Monitoring
Automated adversarial tests, drift detection and monitoring integrated into ML CI/CD pipelines.
This project focuses on integrating security and observability directly into the ML pipeline lifecycle. Pipelines automatically perform adversarial tests on new model commits, detect drift during retraining, and visualize performance degradation metrics on dashboards.
Tech Stack
- GitLab CI/CD · Jenkins · Prometheus · Grafana
- Adversarial unit tests & drift detection modules
- Automated rollback and alerting via webhooks
Key Features
- Model Security Testing: Runs adversarial validation on each commit to detect robustness regressions.
- Data Drift Detection: Compares new data distributions with training data to prevent silent model decay.
- Continuous Monitoring: Exposes metrics to Prometheus, with Grafana dashboards for accuracy and loss over time.
- Automated Response: On metric threshold breach, pipelines can trigger rollbacks or retraining jobs.