MLOps Cyber-Defense Dashboard
Real-time fraud detection powered by automated ML pipelines
Financial institutions process millions of transactions daily. I architected an automated ML pipeline that detects fraudulent patterns in real-time while explaining decisions to L1 analysts using LLM-powered insights.
The Challenge
Traditional rule-based fraud detection systems generate thousands of false positives daily, overwhelming analyst teams. The challenge was building an ML system that not only detected threats accurately but could explain its decisions in plain language for non-technical operators.
Scale
Process 50M+ transactions per day with <50ms latency.
Automation
Automated model retraining when drift is detected.
Explainability
Real-time alerting with LLM-powered explanations.
Reliability
Zero-downtime deployments during model updates.
Technical Architecture
Pipeline Flow
- Transaction Ingestion: Kafka streaming pipeline for real-time data.
- Feature Engineering: Real-time feature calculation and vectorization.
- Model Serving: FastAPI inference endpoint with <50ms latency.
- Explanation Layer: LangChain retrieving context for LLM capability.
- Monitoring: Prometheus + Grafana tracking model drift.
- Retraining: Automated GitHub Actions workflows.
Product Support Strategy
"Supporting this system meant building comprehensive observability. I created L3 runbooks for model degradation, built API integration guides, designed alerting thresholds, and developed an 'explainability dashboard' so L1 analysts could understand model predictions. This led to a 40% reduction in false positives and 60% faster incident resolution."