Overview
This lecture covers the end-to-end architecture, setup, and practical implementation for building a production-ready fraud detection pipeline using cloud-based data engineering and data science tools.
System Architecture and Process Flow
- The system ingests (synthetic or real) data via a Python producer into an Apache Kafka cluster (Confluent Cloud recommended for scale).
- Data labeled for fraud (typically 1-2% fraudulent) is streamed for model training and inference.
- Apache Airflow orchestrates periodic model training (e.g., 3:00 a.m. daily) using new data from Kafka.
- Trained models and experiment metadata are tracked and versioned in MLflow, with artifacts stored in MinIO (S3-compatible).
- Apache Spark performs real-time inference on new transactions from Kafka, with predictions written to an output topic for downstream actions (e.g., alerts, dashboards).
Local and Production Requirements
- Local: Minimum 4 CPU cores, 16GB RAM (8GB acceptable), 100GB+ SSD/HDD, Python 3.10, Docker, and recommended libraries (XGBoost, pandas, scikit-learn, Airflow).
- Production: 16+ cores, 64GB+ RAM, 1TB+ storage, NVIDIA GPU for deep learning; use cloud services (AWS EC2/SageMaker, Google Cloud AI Platform, Azure ML) as needed.
Key Tools and Components
- Docker: For containerization and orchestration of all services.
- Apache Kafka: Cloud-based event streaming platform (Confluent Cloud suggested).
- Airflow: Orchestration tool for scheduled/automated model training and retraining (Celery executor with PostgreSQL).
- MLflow: Handles experiment tracking, model registry, and versioning; stores artifacts in MinIO.
- MinIO: S3-compatible storage for model artifacts.
- Spark: Real-time streaming inference engine.
- Other: Redis (message broker for Celery), Flower (task monitoring), PostgreSQL (metadata storage for Airflow/MLflow).
Data Engineering and Modeling Pipeline
- Data producer simulates transactions with realistic fraud labels/patterns (account takeover, card testing, merchant collusion, geographic anomaly).
- Categorical and temporal feature engineering for improved fraud detection.
- Imbalanced data handled via SMOTE or similar oversampling.
- Model selection based on best precision/accuracy/F1; only higher-performing models promoted to production.
- XGBoost used for classification; alternatives like CatBoost or LightGBM can be compared.
- Hyperparameter tuning performed for optimal results.
- All results, metrics, and models are versioned and logged in MLflow and MinIO for traceability.
System Setup and Debugging
- Docker Compose used to configure services; ensure all services (Airflow, MLflow, MinIO, Kafka, Spark) are properly networked and volumes are mapped.
- Validate environment before training (e.g., check MLflow and MinIO connectivity, Kafka config).
- Common issues include Docker resource limits, permission errors, and version mismatches—address by checking logs and configuration files.
Key Terms & Definitions
- Kafka — Distributed streaming platform for real-time data pipelines.
- Airflow DAG — Directed Acyclic Graph; orchestrates workflows in Airflow.
- MLflow — Open-source platform for managing ML lifecycle (experiment tracking, model registry).
- MinIO — On-premises S3-compatible object storage.
- SMOTE — Synthetic Minority Over-sampling Technique to rebalance datasets.
- Hyperparameter Tuning — Searching for best model parameters.
- Feature Engineering — Creating informative input variables from raw data.
Action Items / Next Steps
- Set up the Docker Compose architecture and verify all services run as expected.
- Implement and run the data producer to populate Kafka with synthetic transactions.
- Configure Airflow DAG for scheduled model training and ensure MLflow/MinIO artifact tracking.
- Develop and test model-building (XGBoost pipeline with feature engineering, SMOTE, hyperparameter tuning).
- Test Spark-based inference on new Kafka data and write predictions to output topics.
- Regularly monitor model performance and update production models as new, better versions are trained.