Checklist

Model Governance & MLOps Checklist for OT Pilots

An actionable, audit-ready interactive checklist to run safe OT model pilots. Each item captures completion, owner, target date, evidence, and notes so teams can deploy incrementally, monitor drift, and retain governance artifacts.

Interactive Tool

Model Governance & MLOps Checklist for OT Pilots

This checklist helps run short, shop‑floor focused experiments that prove safe, auditable model lifecycle practices for OT. For each checklist item, mark completion, assign an owner, set a target date (YYYY‑MM‑DD), attach an evidence link or artifact ID, and add concise notes describing acceptance criteria or artifacts. Save responses to create an audit trail for governance and scaling decisions.

Validate sensor ranges, units, timestamp alignment, missing-value handling, and data type/schema conformity against the model contract.
Person responsible for data validation and acceptance.
Planned date to complete validation.
Link to validation script, dataset snapshot, dataset hash, or ticket.
Acceptance criteria (e.g., allowable missing %; unit tests passed).
Define baseline metrics (precision/recall, RMSE, classification accuracy, latency) measured on representative OT data and acceptable ranges in production.
Numeric baseline (use context in notes for metric name/units).
E.g., F1 score, RMSE (units), median latency (ms).
Dataset used, sampling method, and test harness or script IDs.
Person accountable for baseline and ongoing measurement.
Provide short, human-friendly guidance on model outputs, limits, and expected actions for operators and supervisors.
Model cards, decision flowcharts, SHAP/feature importance reports, or runbooks.
Person responsible for operator guidance and documentation.
Describe what to show operators (quick checks, confidence thresholds, known failure modes).
Define numeric thresholds, severity levels, who to notify, and response playbooks for alerts and anomalies.
List threshold values, channels (pager, email, dashboard), and escalation sequence.
Person or role owning alert definitions and testing.
Define drift detection signals, retraining frequency or event triggers, and how training datasets are curated and retained for traceability.
E.g., concept drift detected when metric falls X% vs baseline; retain N months of raw data; anonymization notes.
Person responsible for retraining decisions and pipelines.
Confirm safe rollback plan, rollback automation limits, manual override steps, and expected RTO (recovery time objective).
Steps, checkpoints, and verification after rollback. Include toggles, feature flags, or deployment tags to revert.
Person empowered to execute rollback and confirm safe state.
Ensure inference logs, input snapshots (where permitted), model version, and dataset lineage are captured and retained for troubleshooting and audits.
Link to logging system, dashboard, or example logs demonstrating fields captured.
Person responsible for telemetry and dashboards.
Confirm least-privilege access to models, secrets management, credentials rotation, and OT/IT segmentation rules.
Firewall rules, IAM policy, or audit report.
Security or OT systems owner.
Confirm stakeholders have reviewed risks, acceptance criteria, rollback plan, and monitoring; store sign-off artifact ID or link.
Meeting minutes, signed checklist PDF, or change control ticket.
Person or role providing final go/no-go for the pilot.
Operators can interpret outputs, follow runbook steps, and perform manual overrides and safety procedures.
Attach or link short runbooks and training artifacts.
Person accountable for operator readiness.
Document remaining risks, mitigations, and acceptance by stakeholders.
List residual risks and mitigation status.
Risk owner for the pilot.
Final confirmation that the pilot may proceed under defined controls.
Person giving final sign-off.
Date of final sign-off.
Anything else the team should record (e.g., links to model registry, dataset hashes, or experiment IDs).
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