Model Governance & MLOps for OT — Research Project

Practical templates and a short research plan to pilot safe model deployment, monitoring, rollback and governance on the shop floor.

Why this matters for manufacturing and operations

Manufacturers are adopting machine learning for inspection, predictive maintenance, scheduling and quality. In OT contexts, models interact with equipment, operators and safety systems—so mistakes or silent degradation are not just data problems, they can affect uptime, product quality and worker safety. This resource helps teams run small, measurable trials that prioritize reliability, traceability and operator control before scaling to production.

What this resource helps you understand and accomplish

Use the included research plan and templates to:

• Design a focused pilot tied to an operational KPI (e.g., reduce false alarms, improve first‑pass yield, or shorten reactive maintenance time).
• Deploy models safely using shadow, canary, or phased rollouts and document the deployment plan.
• Define monitoring signals (performance, drift, data quality) and alerting thresholds that matter to operators and engineers.
• Create explicit rollback and operator‑override procedures so staff can stop or revert model actions quickly.
• Capture governance artifacts: model lineage, validation checklists, versioning notes, and decision logs for audits and continuous improvement.

Who benefits

This project is designed for cross‑functional plant teams: plant managers, supervisors, maintenance and reliability engineers, quality leads, industrial data scientists, automation integrators, and safety or compliance owners. Consultants and solution integrators can use the templates to run predictable, responsible pilots for customers of any size.

Practical examples — how teams might use it

Examples include running a predictive‑maintenance model in shadow mode to compare predicted faults against actual repairs before allowing the model to influence maintenance work; trialing a visual‑inspection model on a single line with standard lighting and operator feedback loops; or piloting an OEE‑focused scheduling recommendation in read‑only mode while monitoring downstream impacts.

How to use the materials in this project

The package includes checklists and governance templates you can copy and tailor. Start small: choose one use case, run a time‑boxed experiment, use the deployment checklist to record readiness, and use the governance template to log model versions, validation results and rollback triggers. Consider converting the checklists into interactive forms to capture pilot results (the platform supports saved checklist responses and JSON storage), and copy the project into your site or plant domain so you can adapt it to local equipment, rules and regulations.