Fail closed
Missing evidence blocks promotion.
Projects / CreditScoreV4 ML Governance
Versioned release2026-09Synthetic ML governance case study
CreditScoreV4 ML Governance
CreditScoreV4 demonstrates independent fail-closed controls for quality, drift, fairness, integrity, staged promotion, rollback and incident evidence.
Problem
Aggregate model accuracy can hide unsafe data changes, subgroup harm, corrupted artifacts, or an unhealthy rollout.
Ownership
Designed the synthetic scenario, control boundaries, serving contract, failure fixtures, evidence pipeline, release gates, monitoring and gated infrastructure path.
Primary proof
Tagged v1.0.1 release with independent fail-closed controls
01 / Overview
System and intended use
A synthetic portfolio reference for teams evaluating ML APIs, validation, monitoring, governed releases and production-readiness controls.
v1.0.1Release
Fail-closedGovernance
Canary + rollbackRollout
AWS gatedDeployment
02 / Architecture
System boundary and flow
System flow04 stages
Validation gatesEvidence checks
Integrity-checked registryArtifact trust
Shadow → canaryStaged promotion
Monitoring → rollbackHealth response
03 / Engineering decisions
Key design choices
Independent controls
One aggregate score cannot hide another risk.
Verify before loading
SHA-256 integrity is checked before deserialization.
04 / Verification
Claims, evidence and boundaries
| Claim | Inspectable Evidence | Boundary |
|---|---|---|
| The governance system has a versioned release. | The repository exposes the supplied v1.0.1 release link. | The project uses deterministic synthetic data and is not real lending production use. |
| Independent controls exercise fail-closed behavior. | The project includes deterministic failure scenarios for data quality, drift, fairness, integrity, promotion and rollback controls. | These engineering controls are not regulatory certification. |
| Model integrity is checked before loading. | The serving path verifies the model artifact SHA-256 before deserialization. | Integrity verification does not validate model fairness or business suitability by itself. |
| Offline and online scoring share a contract. | The documented implementation uses one scoring contract across evaluation and serving boundaries. | Contract consistency does not remove distribution-shift or data-quality risk. |
05 / Boundaries
Limitations and claim boundaries
- Data and failure scenarios are synthetic, not real lending production use.
- Engineering guardrails are not regulatory certification.
- AWS delivery remains gated by default.
06 / Transferable capability
Related client problems
The work demonstrates how to convert an ML artifact into a validated API and design observable, fail-closed release controls without claiming regulatory approval.
Relevant to: ML serving, monitoring, data quality, drift detection, governed releases and production-readiness reviews.
Discuss a Similar Project →07 / Technical depth
Engineering appendix
Review implementation, failure modes, delivery and trade-offs
Implementation
- Built deterministic validation, drift, fairness, performance, artifact-integrity, promotion and rollback controls around one scoring contract.
- Added a FastAPI serving path, Prometheus/Grafana observability, CI security gates, failure fixtures and a disabled-by-default AWS path.
Failure modes and controls
- Contract failures quarantine unsafe batches.
- Intersectional checks expose subgroup harm.
- Canary health supports rollback.
Delivery and operations
- FastAPI with Prometheus and Grafana
- GitHub Actions quality and security gates
- AWS path gated and disabled by default
Engineering trade-offs
- Independent gates produce clearer failures but require more evidence and operational upkeep.
- Deterministic fixtures make controls reproducible but cannot reproduce all real lending conditions.
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