Checklist

Model Governance & MLOps Checklist for OT

An interactive, shop-floor focused checklist to pilot safe, auditable model deployment, monitoring, rollback and governance in OT environments. Collects key decisions, owners, monitoring settings, rollback plans, data and lineage references, and governance sign‑off so teams can run short experiments while preserving safety and traceability.

Interactive Tool

Model Governance & MLOps Checklist for OT

This checklist helps OT teams deploy machine learning models in a controlled, auditable way. Use it to record the model identity, owners, deployment environment, readiness checks, monitoring configuration, rollback and operator‑override plans, retention and lineage references, and governance signoff. Complete one form per model deployment or promotion (e.g., staging → production). Where evidence is required (tickets, metrics, registry links), paste links or brief references in the related fields.

Guidance: keep answers concise. If an item is 'No' or 'Not ready', pause the deployment and follow your change control process.

Exact name or registry identifier for the model being deployed.
Version, commit, or artifact identifier to ensure immutable traceability.
One or two sentences describing what the model predicts/controls and the intended operator action.
Person accountable for the model in production (team lead, ML owner, or engineer).
Role or team (e.g., Controls Eng, Data Science, Reliability).
Where the model will run. Production deployments require stricter controls.
YYYY-MM-DD or approximate date for the deployment/promotion.
Record the result of the deployment gate review.
Name of the person who approved the deployment gate.
Have the input data schemas, quality checks, and sample rates been validated for the target environment?
Key metrics and their values from training/validation (e.g., accuracy, MAE, F1). Include expected range.
Link to experiment results, model card, or ticket containing metric artifacts.
Select at least those metrics necessary to detect silent degradation or safety issues.
Describe numeric thresholds or change conditions that should trigger alerts (e.g., accuracy drop > 5%, latency > 300 ms, drift score > 0.2).
Choose how often monitoring signals are evaluated and reported.
Where should alerts be surfaced so operators and owners can act.
Is there a tested, documented rollback or safe‑stop plan that can be executed quickly?
Concise, ordered steps to revert the model or place it into a safe state (who, how, expected time). Include required tickets or console commands.
How operators should respond to alarms caused by the model (stop, bypass, manual control). Keep language clear for floor staff.
Link to the model artifact in your model registry or version control.
Where inputs come from ( historian tag names, schema version, dataset ID ).
How long raw inputs, features and monitoring metrics will be retained for audits and retraining.
How long model decision logs, alerts and sign‑off records will be kept.
Reference to the deployment/change ticket governing this rollout.
Estimated risk if the model misbehaves (safety, quality, production impact).
How the model is constrained from taking unsafe actions.
Name of the person who signs off for production (e.g., Safety Officer, OT Manager).
YYYY-MM-DD when governance sign‑off occurred.
Any other context, links to runbooks, test results, or monitoring dashboards.
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