MLOps Monitoring & Observability Platform
2025 · Terraform, Lambda, GitLab CI/CD, MLflow, Prometheus, SNS, Snowflake, Python
Problem
With 15 models running across 5 countries and 400+ data pipelines, there was no centralized source of truth for model performance. Issues were discovered late — by business stakeholders noticing drops in campaign results, not by the engineering team catching a data drift two weeks earlier.
The missing piece wasn't alerting (there were ad-hoc alerts). It was a coherent monitoring layer with consistent metrics, drift detection that wasn't just threshold-based, and a unified view across all models and pipelines.
Solution
Built a central monitoring layer that tracks model performance, feature and prediction drift, and pipeline health across all deployed models. Drift detection uses statistical process control — control charts on distribution statistics — rather than fixed thresholds, which cuts false positives significantly.
Architecture
Impact & Scale
- 15 models monitored continuously
- 400+ pipelines tracked for health and latency
- Drift detected early via SPC — no more finding out from a business email
- Model performance dashboards used in bi-weekly cross-country reviews
Next Steps
- Automated remediation triggers (restart pipelines, flag for retraining)
- Unified alerting across channels
- Extend to LLM-specific monitoring: prompt drift, output quality degradation