A full-stack MLOps platform that receives datasets, calculates feature-level drift severity, ranks relevant changes and generates HTML, Markdown and JSON reports. The FastAPI backend orchestrates rules, Celery runs analyses and the React dashboard turns signals into operational decisions.
- FastAPI
- React
- Celery
- Redis
- PostgreSQL
- MinIO
- Docker
Context and problem #
Production models lose reliability when data distributions change. The project turns that uncertainty into an observable routine with severity and history.
My contribution #
The case demonstrates asynchronous backend work, dashboarding, persistence, file storage and technical decision documentation in an integrated platform.
Architecture #
FastAPI receives uploads and triggers services; Celery runs the drift engine; PostgreSQL stores metadata; MinIO or local storage keeps files and reports; React exposes history, charts and downloads.
- ✓ FastAPI REST
- ✓ Celery + Redis
- ✓ PostgreSQL + MinIO
- ✓ React dashboard
Evaluation and quality #
The value is making drift measurable per feature and run, enabling comparison across global severity, rankings and action recommendations.
Limitations #
Drift indicates statistical change, not causal model degradation. Retraining decisions still need business impact and model metrics.
Next steps #
Evolve authentication, multi-user support, Prometheus/Grafana observability, scheduling and MLflow integration.
Test the product and inspect the code
The live demo runs the core flow with safe local data. The public repository preserves the complete backend, tests, infrastructure and technical documentation.