Autonomous Security MLOps Platform
Enterprise-grade MLOps + AI-powered Security Inference System
Enterprise-grade MLOps + AI-powered Security Inference System
Overview
Production-ready MLOps platform demonstrating enterprise-grade ML engineering at scale. The system features 5 ensemble ML models for detecting anomalous security patterns, 50+ engineered features, comprehensive monitoring with drift detection, statistical A/B testing framework, and full infrastructure-as-code deployment.
Showcasing real-world MLOps patterns and production safety practices with comprehensive documentation, advanced monitoring, and scalable infrastructure design.
Key Features
5 Ensemble Models
XGBoost, LightGBM, CatBoost, Stacking, and Voting ensembles with automatic best-model selection and feature importance tracking.
50+ Engineered Features
Temporal, behavioral, sequence-based, and attack pattern features with automated selection using SelectKBest and mutual information.
Advanced Monitoring
Evidently AI for drift detection, SHAP for explainability, real-time anomaly detection, and comprehensive performance tracking with alerts.
🧪Statistical A/B Testing
Two-proportion z-test, Welch's t-test, Mann-Whitney U test with sample size calculation, power analysis, and experiment tracking.
☸️Production Infrastructure
Kubernetes with auto-scaling (3-10 replicas), Docker Compose stack with 8 services, persistent storage, network policies, and secrets management.
Production Safety
3-tier fallback system (Prod → Staging → Safe Mode), canary deployments, health checks, rate limiting, and API authentication.
System Architecture
Data Pipeline
Security logs → Schema Validation (Pydantic) → Data Quality Checks (Great Expectations) → Feature Engineering (50+ features) → DVC Versioning
Model Training & Selection
5 Ensemble Models (XGBoost, LightGBM, CatBoost, Stacking, Voting) → MLflow Tracking → Automatic Best-Model Selection → Model Registry → Canary Evaluation → Deployment
Inference Service
FastAPI with Hybrid Risk Scoring → Rate Limiting (100 req/min) → API Authentication → Prometheus Metrics → Health Checks → Auto-Scaling
Monitoring & Observability
Evidently AI (Drift Reports every 6 hrs) → SHAP Explanations → Real-time Anomaly Detection → Prometheus + Grafana Dashboards → Alert System → Auto-Retrain Triggers
Technical Metrics
92-95%
F1 Score
<100ms
Inference Latency (p95)
99.9%
Uptime Target
5
Ensemble Models