Checklist

MLOps for OT: Deployment & Monitoring Checklist (Interactive)

An actionable, shop-floor focused checklist and evidence-capture form to pilot safe model deployment, monitoring, rollback, and governance in OT environments. Use during pre-deployment reviews, canary rollouts, and post-deployment audits to ensure models remain safe, reliable, and auditable.

Interactive Tool

MLOps for OT: Deployment & Monitoring Checklist

This interactive checklist helps teams run short, shop-floor focused experiments and pilots that prove safe, auditable model lifecycle practices for OT. Use it for pre-deployment reviews, canary rollouts, and post-deployment checks. Capture evidence, record thresholds, and save governance artifacts for audits. Adapt thresholds and items to your process and risk profile.

e.g., model name, version tag, artifact hash or registry path
Dataset ID/version, preprocessing notes, feature set, and links to lineage artifacts
Choose the rollout approach for this pilot
Have unit, integration, safety, and dry-run tests been executed and passed? Attach evidence in Notes.
Record key baseline metrics (e.g., accuracy, precision/recall, latency, false positive rate, throughput) and expected ranges.
If deploying canary, what percentage of devices/processes will receive the model? Leave blank if not applicable.
Is drift detection configured for input distribution and output performance?
Specify thresholds, statistical tests, evaluation windows, and retrain/alert triggers (e.g., KL-divergence > 0.2 over 24h).
List what will be monitored (e.g., prediction distribution, confidence, OEE impact, latency, error rates) and their SLO/SLA targets.
Define alerts, severity levels, channels (ops, safety, QA), and response expectations (who, when, escalation).
Is there a tested automated and a manual rollback/stop procedure?
Concise steps to revert model version, restore safe defaults, and confirm system state after rollback.
Can operators safely override model-driven actions? Are prompts, warnings, and required confirmations clear?
What deterministic safe state will the system enter on model failure or loss of signal?
Select which artifacts are attached or available in the project record.
Are model artifacts, credentials, endpoints, and logs access-controlled and logged?
Has FMEA, HAZOP, or equivalent been completed for the model use-case?
When is the first review, who will participate, and what metrics/behaviours will be evaluated?
Name, role, and contact for escalation (e.g., site ML owner).
Links to logs, tickets, dashboards, or attachments. Record deviations from the standard process.
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