Checklist

MLOps for OT: Deployment, Monitoring & Rollback Checklist

An interactive checklist to guide safe deployment of machine learning models in OT environments, record monitoring and rollback plans, and capture ownership, thresholds, and post-deployment checks.

Interactive Tool

MLOps for OT Checklist

Purpose: Deploying models into operational technology (OT) systems requires clear approvals, measurable baselines, practical monitoring, and robust rollback paths. Use this checklist to record decisions, thresholds, owners, and follow-up actions so small experiments remain safe and learnable.

This form captures essential deployment, monitoring, and rollback items. For each required question, provide evidence, thresholds, or responsible roles when available. Saved responses become part of the project record for audits, post-mortems, and continuous improvement.

Has the model been approved by an owner, with a documented performance baseline and a safety review?
Who signed off on approval? Use role if individuals rotate (e.g., Process Owner, Safety Lead).
Describe baseline metrics, expected ranges, and acceptance thresholds (e.g., precision >= 0.85, FP rate <= 2%). Include how the baseline was measured and dataset used.
Is there a documented plan describing staging environment, canary rollout, validation steps, and rollback criteria?
Initial percent of devices/traffic for canary (e.g., 5). Leave blank if not using canary.
List conditions or thresholds that require human review or intervention (e.g., any high-risk decision, ambiguous classification, or when confidence < X).
Are drift detectors, baselines, and alerts configured for model predictions and input distributions?
Specify which drift metrics (e.g., PSI, KS, feature distributions) and alert thresholds will trigger investigation or rollback.
Are checks for missing fields, invalid ranges, stale timestamps, and sensor outages active and mapped to alerts?
Are inference latency and system performance monitored against SLOs? Include degradation thresholds.
Define concrete, measurable conditions that will automatically trigger rollback (e.g., false positive rate > X for Y minutes; latency > Z ms).
Describe how operators can immediately disable the model, who to contact, and the escalation chain during incidents.
Are model, data, and configuration versions logged, and are decisions and alerts retained for audit and retrospective analysis?
List any regulatory, contractual, or customer obligations that affect deployment, data retention, or auditability.
Summarize hazard analysis, mitigations, residual risks, and any safety interlocks tied to the model.
How often will automated and manual checks run after deployment?
List outstanding actions, owners, and due dates (e.g., tune threshold, add sensor health checks).
Operator-estimated residual risk after mitigations. Use to prioritize follow-up controls.
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