Regulators have been clear for years: a model in a regulated decision is a source of risk that must be managed like any other. SR 11-7 in the US and the EBA’s guidance in Europe expect validation, ongoing monitoring, documentation and clear ownership. Modern MLOps gives you the machinery to meet those expectations — if it’s built for governance, not just deployment.
The pillars of model risk management
- Validation — independent review of a model’s conceptual soundness, data and performance before it goes live.
- Explainability — the ability to justify individual decisions, via SHAP or equivalent.
- Ongoing monitoring — drift detection (has the population or relationship shifted?), performance tracking, and thresholds that trigger review.
- Documentation and versioning — a reproducible record of what model, trained on what data, produced what decision.
- Ownership — clear accountability for each model’s lifecycle.
Where MLOps meets compliance
KeplerAI treats these as first-class, not add-ons. Scenarios are versioned and reproducible. Monitoring surfaces score distributions and drift. SHAP explanations attach to decisions. Documentation is generated from the notebooks and scenarios that produced the model, so the paper trail matches the artifact.
Governance without moving data
Here’s the twist most MLOps platforms miss: model governance shouldn’t require centralizing all your data. In KeplerAI, models train and score inside your data plane, and the weights stay there. Governance metadata — metrics, lineage, drift — flows to the control plane; the sensitive artifacts don’t.
The outcome
A model file your validators can read, monitoring that catches degradation early, and a documentation trail that satisfies SR 11-7 and EBA reviewers — without turning your data lake into an export. Governed models, sovereign data. Read about data quality profiling, the input side of the same discipline.