Model Governance & MLOps Checklist for OT Deployments

Purpose

This playbook helps teams run short OT-focused experiments that prove safe, auditable model lifecycle practices. Use it to ship small, incremental model-driven features to the shop floor while protecting safety, reliability, and operator trust.

How to use this playbook

Treat this as a living deployment checklist. Complete required items before moving from one stage to the next. Record decisions, artifacts, and signatures so the pilot becomes an auditable asset that can be scaled or retired safely.

Deployment stages

1. Design & readiness
2. Pre-deployment testing in staging
3. Pilot (canary) on limited devices/lines
4. Gradual rollout or rollback
5. Production monitoring and periodic review

Core checklist (complete and attach artifacts)

1) Model registry & version control

• Model artifact stored in a registry with immutable version IDs. (Artifact location: URI)
• Training code, hyperparameters, preprocessing, and feature definitions versioned alongside artifact. (Repo/commit: string)
• Train/validation/test datasets identified with checksums and lineage info. (Dataset IDs and checksums attached)
• Owner & approver declared (Model Owner, Data Owner, OT Owner). See Owner Matrix below.
• Security classification and access controls applied to artifact and datasets.

2) Data readiness & safety gates

• Input data schema and ranges validated; missing-value behavior defined.
• Feature distribution and covariate checks run; summary report attached.
• Label quality verified (sampling audit); acceptable label error rate defined.
• Adversarial or out-of-range input behavior tested and bounded (e.g., saturating outputs, safe defaults).
• Fail-safe behavior defined when input sensors fail or stop reporting.

3) Pre-deployment integration tests & safety gates

• Unit tests for model wrapper and preprocessing executed with pass criteria.
• Integration tests in a staging environment that mirrors OT interfaces completed (latency, throughput, serialization/comms).
• Closed-loop simulation (if available) to validate output actions do not violate safety constraints.
• Business-rule and safety-rule cross-checks where model outputs are validated against deterministic safety guards.
• Approval checklist signed by OT safety lead, quality lead, and model owner before pilot begins.

4) Runtime monitoring & metrics

Define dashboards and alerts that run continuously. Attach dashboard snapshots and metric definitions.

• Prediction quality metrics: accuracy, precision/recall (for classification) or MAE/RMSE (for regression) on labeled pilot samples.
• Data drift metrics: population drift (KL/divergence), feature distribution shifts, input coverage gaps.
• Operational metrics: latency, error rates, throughput, CPU/Memory usage of model service.
• Safety metrics: number of safety-rule overrides, near-miss events attributed to model outputs, operator interventions.
• Business KPIs: impact on OEE, throughput, scrap rate, or downtime — measured against baseline and confidence intervals.

5) Alerting & human‑in‑the‑loop fallbacks

• Define alert thresholds for each key metric and the on-call rotation for alerts.
• Human-in-the-loop fallback behavior documented: when to require operator confirmation, when to present advisory outputs only, and when to block automated actions.
• Operator UI messaging templates (clear, non-technical) and override procedures included in runbook.
• Record how operator overrides are logged and linked to model versions for post-incident analysis.

6) Rollback & retraining triggers

• Explicited rollback triggers (examples): detection of model-driven unsafe action, sustained KPI degradation beyond defined threshold, sudden drift metrics exceeding threshold, unexplained increase in operator overrides.
• Rollback plan documented with steps, required approvals, and expected RTO (recovery time objective).
• Retraining triggers documented (schedule-based, performance-based, or data-volume-based) and initial retraining plan attached.

7) Documentation & owner matrix

Attach or link to the following artifacts before pilot begins.

• Model card (purpose, limitations, evaluation summary, training datasets).
• Risk assessment and mitigation log.
• Runbook with step-by-step operator procedures and rollback steps.
• Audit log plan: what is logged (inputs, outputs, model version, operator actions), retention period, and where logs are stored.
• Owner matrix (example):

8) Pilot plan & acceptance criteria

• Scope: devices/lines included, duration, sampling frequency for labeled checks.
• Success criteria: specific KPI improvements or no significant KPI regressions (quantified), maximum acceptable operator override rate, maximum tolerable false positives/false negatives.
• Exit criteria: conditions to expand rollout, to pause and investigate, or to rollback permanently.

9) Post-deployment review & continuous governance

• Weekly review cadence during pilot including ML, OT, Ops, and Quality representatives.
• Monthly governance review to assess model drift, retraining needs, and compliance artifacts.
• Retention schedule for model artifacts and logs for audit purposes.

Quick runbook excerpt (operator-facing)

When model advises action: display advisory with confidence band and recommended action. Include a clear, single-button override that requires a brief reason entry. Log the override with timestamp and operator ID.

If model fails or behaves unexpectedly: stop automated actions, switch to human control mode, notify on-call OT engineer, and follow rollback steps in runbook.

Common pitfalls and how to avoid them

• Deploying without a rollback plan — always define steps and responsible people before pilot.
• Insufficient labeled pilot data — sample and label during pilot to measure real-world performance.
• Ignoring operator workflows — involve operators early, build simple UIs, and log overrides for learning.
• Not monitoring drift — set baselines and automated drift detection to trigger investigation.

Attachments & templates to include

• Model Card template
• Pre-deployment test report template
• Pilot acceptance form
• Runbook template with rollback checklist
• Monitoring dashboard spec (metrics & thresholds)

Next steps and scaling

After a successful pilot that meets acceptance criteria, formalize the artifacts into a repeatable deployment package: model artifact + tests + runbook + monitoring spec + owner signoffs. Consider packaging this as a reusable site-specific toolkit so other lines or plants can adopt it with minimal tailoring.

Minimum artifacts to store for audit

• Model registry entry and version
• Pre-deployment test report
• Pilot acceptance form with signatures
• Runbook with rollback steps
• Monitoring dashboard snapshots for first 30/90 days

Where this playbook connects to platform capabilities

This checklist is designed to be a living, ownable playbook in an adaptive domain. Consider converting the checklist into an interactive form to capture run decisions, artifact links, and signatures so deployments are auditable and searchable.