SentinelAI
SentinelAI is a distributed AI reliability and monitoring platform designed to:
- Detect model drift
- Monitor inference anomalies
- Track LLM hallucination risk
- Provide real-time observability
- Automate AI governance workflows
It combines statistical ML monitoring with LLM-powered incident intelligence.
Architecture
User → Go API → Snowflake → Drift Engine (C++) → MLflow/SageMaker → LLM Guard → Streamlit Control Plane
| Metric | Value |
|---|---|
| PSI Detection Threshold | 0.20 |
| P95 API Latency | 180ms |
| Throughput | 150 RPS |
| Drift Engine Compute | <2ms |
| LLM Summarization | ~1.2s |
Quickstart
Build Drift Engine cd drift-engine g++ drift_engine.cpp -o drift_engine ./drift_engine
Run Go Ingestion Service
cd ingestion-service go run main.go
Launch Streamlit Dashboard
cd streamlit-dashboard streamlit run app.py
Run ML Training
cd training-pipeline python train.py
Infrastructure
Provision AWS resources:
cd terraform terraform init terraform apply
Deploy to Kubernetes: helm install sentinel ./helm/sentinel
To achieve sub-millisecond statistical scoring at scale.
Go provides efficient concurrency and low-latency HTTP handling.
Cloud-native warehouse for scalable feature storage and SQL-based anomaly analysis.
Experiment tracking, reproducibility, and version control.
LLM-powered root cause summarization and RAG over historical incidents.
Horizontal scaling and production-grade orchestration.
Reproducible infrastructure as code.
Enterprise Value
SentinelAI demonstrates:
- AI system lifecycle management
- Drift monitoring
- MLOps integration
- Distributed systems engineering
- Cloud-native architecture
- LLM augmentation
- Observability & metrics-driven design
This project models production-level AI governance systems used in large-scale environments.
Roadmap
- Add automated retraining pipeline
- Add Prometheus + Grafana dashboards
- Add Shadow Model Deployment
- Add Cost Optimization Engine
- Add Hallucination Classifier Model