MV Grid Fault Risk Scoring Platform

Live docs: https://www.neuromorphicinference.com/demos/mv-grid-fault-risk/
Proof Ledger: https://www.neuromorphicinference.com/evidence/#mv-grid-fault-risk
API docs (live): https://mv-grid-fault-risk-api.onrender.com/docs
Model artefact (release): https://github.com/nepryoon/mv-grid-fault-risk/releases

Production-first, end-to-end ML system for predicting medium-voltage (MV) grid fault risk and prioritising preventive interventions.

Keywords: scalable ML pipelines, CI/CD for ML, feature engineering, artefact versioning, model serving, inference, monitoring-ready outputs, automated retraining, FastAPI, Docker, MLflow.

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What this system does

• Builds a risk score for MV assets/segments from structured operational data.
• Produces monitoring-ready outputs (predictable schema, stable interfaces).
• Ships a serving layer (API) and a demo UI for interactive scoring.
• Tracks experiments and artefacts for reproducibility and rollback.

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Architecture (end-to-end)

Raw data  →  Validation  →  Feature engineering  →  Train/Evaluate  →  Artefact registry
  |                                                    |                   |
  └───────────────>  Training table (versioned)        └── MLflow tracking  |
                                                                  |        |
                                                                  v        v
                                                           FastAPI serving → Demo UI
                                                                  |
                                                                  v
                                                         Monitoring-ready outputs