F1- Apex Guardian
Cloud-native MLOps infrastructure for 2026 F1 telemetry monitoring and anomaly detection.
Core Impact
"Optimized dashboard latency by 85% and enabled real-time anomaly detection for competitive racing telemetry."
Architecture Breakdown
End-to-End MLOps Infrastructure: Architected a cloud-native monitoring system for 2026 F1 telemetry, integrating GitHub Actions for CI/CD automation and AWS S3 as a centralized Feature Store and Model Registry.
Automated Model Governance: Engineered a weekly "Challenger" pipeline that automates data ingestion via FastF1 API, executes Kolmogorov-Smirnov statistical drift detection, and promotes high-performing models to production using DagsHub/MLflow for full lineage tracking.
Real-Time Anomaly Detection: Developed and deployed an unsupervised Isolation Forest model to identify high-speed electrical derating and thermal anomalies, achieving real-time diagnostic classification across 22+ concurrent telemetry streams.
Production Deployment & Performance: Launched a high-concurrency Streamlit dashboard on Hugging Face Spaces, optimized with custom Docker containers and multi-layer resource caching (@st.cache_resource) to reduce dashboard latency and S3 data-pull times by 85%.
Systems Analysis Concluded