GigaFlow MLOps Documentation
GigaFlow is a production-grade, end-to-end MLOps platform for real-time sentiment analysis, emotion detection, language identification, and toxicity screening. It processes live text streams through Apache Kafka, runs multi-model inference, and stores results with full observability.
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Key Features
- Dual-model sentiment + emotion analysis (98.5% accuracy, 28 emotions)
- Multilingual support (16+ languages for sentiment, 6+ for emotions)
- Language detection and toxicity screening
- A/B testing with configurable traffic splitting
- Model explainability with word-level importance highlighting
- User feedback loop for continuous improvement
- Auto-retraining triggered by sustained data drift
- API authentication, rate limiting, and Redis caching
- Batch prediction endpoint (up to 100 texts per request)
- Real-time streaming from Kafka with live Streamlit dashboard
- Full observability with Prometheus metrics and Grafana dashboards
Tech Stack
| Component | Technology |
|---|---|
| ML Models | HuggingFace Transformers (ModernBERT, RoBERTa, XLM-R) |
| Inference | FastAPI + async Kafka consumer |
| Streaming | Apache Kafka |
| Model Registry | MLflow 2.9 |
| Data Versioning | DVC + MinIO |
| Database | PostgreSQL 14 |
| Caching | Redis 7 |
| Dashboard | Streamlit |
| Drift Detection | Evidently AI |
| Monitoring | Prometheus + Grafana |
| Container Orchestration | Docker Compose / Kubernetes |
| CI/CD | GitHub Actions |