README.md

metadata

title: Race Telemetry
emoji: 🏁
colorFrom: green
colorTo: blue
sdk: docker
app_port: 8501
tags:
  - streamlit
  - mlops
  - motorsport
  - telemetry
  - machine-learning
pinned: false
short_description: Real-time motorsport telemetry ML  — Pit Wall Dashboard

🏁 Race Telemetry — Pit Wall Dashboard

This is a lightweight UI demo. The full production MLOps platform is on GitHub → nasim-raj-laskar/Race-Telemetry

Streams pre-recorded race telemetry and runs three ML models in real-time:-

Model	Type	Predicts
Lap Time Predictor	XGBoost Regression	Lap time (seconds)
Gear Optimizer	Random Forest	Recommended gear
Driving Behavior	K-Means Clustering	Aggressive vs Smooth

Full System Architecture

The real system includes FastAPI inference, PostgreSQL, MongoDB Atlas training pipeline, MLflow + DagsHub experiment tracking, PSI drift detection, Prometheus + Grafana observability, and CI/CD via GitHub Actions → AWS ECR → EC2.

Demo vs Production

	This Demo	Full Platform
Models	Bundled .pkl	Dynamically loaded from DagsHub
Data	Static CSV	PostgreSQL live fetch
Inference	Direct in Streamlit	FastAPI backend
Monitoring	None	Prometheus + Grafana
Deployment	HF Spaces	AWS ECR + EC2