Evidence memo

NeuroGrid Fault Risk Scoring Platform

This memo captures the full-stack signal: a tabular risk model engineered as a system — data/feature pipeline thinking, CI-minded training flow, versioned artefacts, and production API serving.

Tabular ML Feature Engineering CI/CD for ML Model Serving Artefact Versioning

Signal → Systems → Proof Ledger → NeuroGrid Memo

Open system Back to anchor API docs

CV anchor: /evidence/#mv-grid-fault-risk

What this proves

The objective is not a model score in isolation — it is end-to-end delivery under operational constraints.

End-to-End ML Reproducibility Release Discipline Serving Monitoring-ready Outputs

Full-stack ownership: problem framing → features → model training → artefact packaging → serving endpoint.
Engineering maturity: versioned artefacts, predictable outputs, and deployment-aware structure.
Operational narrative: reliability and maintainability considerations visible to reviewers.

How to verify (60 seconds)

Step 1 — Verify serving

Open the API documentation and confirm the service responds with predictable schema.

Expected: OpenAPI docs load successfully.
Expected: endpoints show request/response structure.

Step 2 — Verify system documentation

Open the system page and confirm the flow is described end-to-end (data → features → training → serving).

Expected: clear pipeline narrative and operational framing.
Expected: links to source and artefacts are present.

Design choices

Public portfolio constraints require predictable behaviour and low operational risk. This system prioritises traceability and deployability signals that generalise to production stacks (CI, registry, monitoring).

Traceability Versioning Operational Checks Rollback Mindset

Production risks & mitigations

Data quality & drift

Mitigation: schema/range checks at ingestion; missingness thresholds.
Mitigation: drift monitoring on key features and prediction distribution.
Mitigation: retraining triggers tied to performance degradation.

Serving reliability

Mitigation: stable request/response contracts and versioned releases.
Mitigation: timeouts, retries, and basic health metrics.
Mitigation: fast rollback to previous artefact version.

Next improvements (production path)

Add full CI gates (data validation, unit tests, evaluation checks).
Introduce a model registry with lineage metadata and model cards.
Add monitoring dashboards for latency, errors, drift and delayed outcomes.
Support canary deployments and champion/challenger evaluation.

Keywords (ATS trigger set)

Tabular ML Feature Engineering CI/CD for ML Model Serving Inference Monitoring Data Drift Retraining Triggers Artefact Versioning

Proof anchor for CV: /evidence/#mv-grid-fault-risk