Machine Learning
Traffic Anomaly Detection System
End-to-end MLOps pipeline for anomaly detection on traffic sensor data, using MLflow and DVC for experiment tracking and reproducibility, model serving containerized with FastAPI and Docker, and Prometheus/Grafana monitoring for real-time metrics and observability.
- MLOps
- MLflow
- DVC
- FastAPI
- Docker
- Prometheus
- Grafana
The infrastructure around the model
The focus here is what surrounds the anomaly-detection model: reproducible training, containerized serving, and production observability, wired together end to end.
Tracking and reproducibility
- MLflow tracks experiments, parameters, and metrics across training runs.
- DVC versions the traffic sensor datasets and model artifacts so a run can be reproduced from the exact data it was trained on.
Serving and observability
- The trained model is containerized and served through FastAPI and Docker.
- Prometheus scrapes serving metrics and Grafana dashboards them for real-time observability and performance tracking in production.