Fraud Detection MLOps System

Concept:
A production-grade, end-to-end MLOps pipeline for real-time fraud detection with automated retraining, monitoring, metrics tracking, and scalable deployment.

Problem Solved:
Enables consistent, reliable, and automated ML model lifecycle management — including training, evaluation, deployment, monitoring, and retraining — ensuring high accuracy and stable performance in a real-time fraud detection environment.

Scope:

• Input:
  Transaction data with numerical, categorical, and behavioral features.

• Process:
  A full MLOps workflow integrating real-time inference, monitoring, retraining, and metric collection:

  1. Fraud Detection API (Flask): Serves real-time predictions via REST.

  2. ML Model (Random Forest): Core training & inference logic.

  3. Airflow DAG: Automates daily model retraining and performance checks.

  4. MLflow Tracking: Logs experiments, versions, metrics, and artifacts.

  5. Prometheus: Collects metrics (latency, fraud rate, prediction counts).

  6. Grafana Dashboards: Visualizes system performance and alerts.

  7. Docker Compose: Orchestrates all services for reproducible deployment.

• Output:
  Real-time fraud prediction API (<120ms latency), Prometheus metrics, Grafana monitoring dashboards, Airflow-managed retraining pipeline, and MLflow-tracked model versions.

Features:

• Real-time prediction API (Fast inference: 80–120ms)

• Automated daily retraining (Airflow)

• MLflow model registry with experiment comparison

• Prometheus metrics for API + model behavior

• Grafana dashboards (fraud rate, latency, prediction volume, etc.)

• Robust data preprocessing and feature engineering

• Class balancing for imbalanced fraud data

• End-to-end containerized setup (Docker Compose)

• Performance monitoring and alerts

• Versioned ML models with automatic deployment of improved variants

Tech Stack:

• ML Framework: Scikit-learn

• API: Flask

• Workflow Orchestration: Apache Airflow

• Monitoring: Prometheus + Grafana

• Experiment Tracking: MLflow

• Infrastructure: Docker, Docker Compose

• Database: PostgreSQL (Airflow metadata)

• Language: Python

System Components:

1. Fraud Detection API

   • /predict, /metrics, /health, /test

   • Real-time inference

   • Metrics collection

   • Input validation

2. Airflow DAG

   • Five-stage retraining pipeline

   • Tasks: performance check, data prep, training, deployment, notification

3. Monitoring Stack

   • Prometheus metrics + Grafana dashboards

4. MLflow Tracking & Registry

   • Parameters, metrics, artifacts, versioning

5. Docker Orchestration

   • All services containerized and networked

Performance:

• Model:

  • Accuracy: 95.2%

  • Precision: 94.8%

  • Recall: 89.3%

  • F1-score: 92.0%

  • ROC-AUC: 96.5%

• API:

  • Latency: 80–120ms

  • Throughput: ~500 req/s (single instance)

  • Availability: 99.9%

• Resource Usage:

  • Memory: ~2GB total

  • CPU: <60% under load

Project Structure:
(High level, no code)

• models/ — Model APIs, loaders, training scripts, artifacts

• airflow/ — DAGs, logs, dependencies

• data/ — Train & test datasets

• prometheus/ — Metrics config

• grafana/ — Dashboards

• mlflow/ — Artifacts, versions

• docker-compose.yml — Full stack orchestration

• Dockerfile / Dockerfile.airflow — Service containers

Output:
Fraud predictions, latency metrics, dashboards, tracked models, logs, alerts, retraining output.

Future Enhancements:

• SHAP explainability

• A/B testing framework

• Data drift detection

• Slack/email notifications

• Authentication & rate limiting

• Kubernetes deployment

• Ensemble modeling (XGBoost + RF)

• Kafka streaming

• Mobile app extension