MLOps & Model Governance Template for OT Deployments

Purpose

This research project defines a lightweight, repeatable approach to trialing MLOps and model governance practices in OT (shopfloor) environments. The goal is to run small, safe experiments that validate deployment, monitoring, rollback, and change-control patterns that preserve safety, traceability, and production continuity while delivering measurable ML value.

Why this matters

Unmanaged models in OT can degrade silently, cause incorrect actuation or recommendations, or create safety/regulatory exposure. Practical governance and lifecycle controls reduce those risks while making it possible to learn from real deployments.

Scope

This plan targets narrow, low-risk shopfloor ML use-cases suitable for controlled trials: anomaly detection on non-safety-facing sensors, quality-prediction models that inform inspections (not automatic rejects), predictive maintenance advisory layers, and monitoring/alerting models. Exclude models that directly control safety-critical actuators unless appropriate safety certification and engineering controls exist.

Desired outcomes (Hungers)

• Deploy models in OT with repeatable governance that preserves safety and traceability.
• Detect and respond to model drift, mispredictions, or performance regressions before production impact.
• Practice emergency rollback and version control so teams can restore safe baseline behavior quickly.
• Document a minimal policy and templates that can be scaled to other workcells or plants.

Mal Hungers (Risks to avoid)

• Uncontrolled model updates or missing monitoring creating production disruptions.
• Loss of traceability for data, model versions, and decisions.
• Failure modes that could indirectly affect safety or regulatory compliance.

Success criteria & measurable metrics

• Operational: zero safety incidents attributable to ML during trial period.
• Reliability: automated detection of significant model drift within N hours of occurrence (configurable).
• Recoverability: average time to rollback to a validated model version under 30 minutes in practice runs.
• Traceability: every prediction in trial linked to model version, data snapshot, and feature lineage.
• Value: measurable improvement in a business metric (reduced false alerts, improved yield, fewer maintenance tickets) vs baseline.

Experiment plan (small trials)

1. Select a low-risk pilot (one machine, one line, or one quality checkpoint) and document baseline performance and failure modes.
2. Prepare a shadow deployment: model runs in parallel, outputs logged but not acted upon.
3. Run shadow for sufficient coverage (time or production volume) to compare predictions to ground truth.
4. Move to canary deployment with a small fraction of decisions exposed to operators or advisory dashboards (no automatic control).
5. Practice rollback and incident drills before expanding scope.

Roles & governance

• Model Owner: accountable for model performance, testing, and change requests.
• OT Lead / Plant Engineer: accountable for safety, interoperability with PLC/SCADA, and deployment approvals.
• Data Steward: responsible for data lineage, privacy controls, and dataset snapshots.
• On-call Incident Lead: responsible for triage, rollback, and post-mortem.
• Change Advisory Board (CAB): small cross-functional review for production model changes during the pilot.

Pre-deployment checklist (template)

• Define model purpose, inputs, outputs, and allowable actions (advisory only vs control).
• Run safety impact assessment; document mitigations for each identified risk.
• Confirm feature and data validation: sensor ranges, null-handling, and timestamp alignment.
• Confirm offline and shadow testing completed with acceptance metrics.
• Establish telemetry endpoints, logging format, and storage for predictions and input snapshots.
• Set alert thresholds and on-call procedures; document rollback runbook.
• Version artifact repositories: model binary, code, training dataset identifier, and configuration manifest.

Monitoring metrics & alerting

Track business, ML, and system metrics. Examples:

• Business: false positive rate, false negative rate, inspection pass rate, throughput impact.
• ML health: prediction distribution changes, confidence score shifts, input feature drift (population stability index), sudden change in error rate.
• Operational: model latency, CPU/GPU utilization, missing input rates, telemetry gaps.

Suggested alert thresholds (examples to adapt):

• Prediction error rate increases by >20% vs baseline over rolling 24 hours — Page incident lead.
• Feature drift PSI >0.2 for any critical input — Trigger investigation and run validating tests.
• Missing telemetry for >5 minutes — Auto-disable advisory outputs and notify OT lead.

Versioning & change control policy (summary)

Every model change must include: semantic version, artifact hash, training data snapshot ID, evaluation report, deployment manifest, and CAB approval for production promotion. Maintain immutable model registry entries and audit logs for deployments and rollbacks.

Rollback & emergency steps (runbook)

1. Detect incident via alerts or operator report.
2. Triage: Isolate model's contribution using shadow logs and short-circuit model outputs to advisory-only.
3. Execute rollback to last validated model version using the deployment manifest (automated if possible).
4. Notify stakeholders and record timeline, root cause hypotheses, and temporary mitigations.
5. Begin post-mortem and remediation plan before next deployment attempt.

Testing checklist

• Unit and integration tests for model-serving code.
• Replay tests: run model on recent historical production data and compare to expected outputs.
• Shadow-run comparison against ground-truth labels where feasible.
• Latency and load tests to ensure timely predictions under expected traffic.

Data management & traceability

Capture and store: input snapshots (hashed), prediction outputs, model version, configuration manifest, and operator actions. Retain data according to regulatory and privacy policy and ensure tamper-evident audit logs.

Deliverables & templates

• Deployment checklist (pre-deploy, deploy, post-deploy)
• Monitoring metrics dashboard spec
• Incident & rollback runbook template
• Model registry entry template (versioning fields)
• Shadow-run comparison worksheet

Timeline & next steps (example 8-week pilot)

1. Weeks 1–2: Select pilot, baseline metrics, and safety assessment.
2. Weeks 3–4: Offline testing, shadow deployment, and monitoring setup.
3. Weeks 5–6: Canary advisory deployment, practice rollbacks and incident drills.
4. Weeks 7–8: Evaluate success criteria, CAB review, and decide scale or iterate.

How to adapt

Scale conservatively: small job shops may run simpler shadow/canary processes and manual rollbacks, while enterprises should automate registry, CI/CD, and telemetry. Document local constraints (connectivity, PLC interfaces, regulatory) and tailor acceptance thresholds.

Suggested attachments (to be added as templates)

Deployment checklist (interactive), Monitoring metric spec (CSV), Rollback runbook (PDF), Model registry template (JSON), Shadow-run comparison worksheet (spreadsheet).

Next actions for the research team

1. Select pilot and assemble roles.
2. Customize the pre-deployment checklist to local OT interfaces and safety rules.
3. Implement telemetry and shadow logging for two production shifts.
4. Run the 8-week pilot and record learnings in the project notebook.