Real-Time Fraud Detection System for Financial Services
Objective:
To improve the observability of the fraud detection system, enabling better monitoring, troubleshooting, and performance optimization of ML models.
Technology Stack:
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ML Models: XGBoost, LSTM
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Logs Management: Loki
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Metrics Collection: Prometheus
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Distributed Tracing: Jaeger
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Deployment Platform: Kubernetes
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Client Interface: Grafana, Kibana
Approach:
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Requirement Analysis: Conducted detailed discussions with the client's IT and data science teams to identify key observability requirements.
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System Design: Designed a comprehensive observability architecture incorporating Loki, Prometheus, and Jaeger.
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Implementation:
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Loki: Integrated Loki for centralized log management, enabling real-time log aggregation and analysis.
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Prometheus: Set up Prometheus to collect and monitor metrics from the ML models and system infrastructure.
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Jaeger: Deployed Jaeger for distributed tracing, providing insights into the end-to-end execution of ML models.
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Integration and Deployment: Deployed the observability stack on a Kubernetes cluster, ensuring seamless integration with the existing ML models and infrastructure.
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Visualization and Monitoring: Configured Grafana and Kibana dashboards to visualize logs, metrics, and traces, providing a unified view of the system's performance and health.
Outcome:
The enhanced observability system significantly improved the client's ability to monitor and troubleshoot their fraud detection models. They achieved a 30% reduction in model downtime and a 40% improvement in troubleshooting efficiency, leading to more reliable fraud detection and enhanced security.