Context & Problem
Deployed ML models silently degrade under evolving data distributions, while common workflows rely on manual, ad-hoc retraining decisions with weak traceability. Existing MLOps solutions either focus on passive drift monitoring or require enterprise-scale infrastructure, making controlled, auditable lifecycle management inaccessible to small teams.
Solution & Approach
Designed and implemented a lightweight, CI-driven MLOps pipeline where data drift is treated as an explicit decision signal rather than a passive metric. Candidate models are trained and evaluated in isolation, tested under predefined drift scenarios, compared against the active production model, and promoted only through rule-based, auditable decisions with human oversight.
Key Highlights
- Operationalized data drift as a first-class control signal influencing evaluation, retraining, and model promotion decisions
- CI-based lifecycle orchestration covering training, evaluation, drift analysis, promotion, deployment, and monitoring
- Rule-based promotion gate: models deploy only if predefined performance and robustness criteria are satisfied
- Reproducible experiments: every model linked to dataset version, preprocessing configuration, training parameters, metrics, and drift diagnostics
- Explicit separation between candidate and production models via centralized model registry
- Downstream monitoring used as decision support, not autonomous retraining, preserving transparency and control
- Reference implementation validated through controlled image classification experiments with simulated data drift