Model governance

Model governance commonly refers to the policies, roles, processes, and technical controls used to manage a model throughout its lifecycle. In industrial and regulated environments, this usually covers how a statistical, optimization, machine learning, or AI model is documented, reviewed, approved, deployed, monitored, changed, and retired.

It includes governance of both the model itself and the supporting artifacts around it, such as training data references, version history, intended use, performance criteria, access permissions, validation records, and change logs. It does not mean the model is always centrally built by one team, and it is not the same as the broader governance of all enterprise data or all software.

What it typically includes

• Defined ownership and accountability for model development, review, approval, and operation

• Documentation of model purpose, scope, assumptions, inputs, outputs, and limitations

• Version control for model logic, parameters, training datasets, and configuration

• Review and validation activities before production use or material changes

• Controls for deployment, access, monitoring, exception handling, and rollback

• Ongoing monitoring for drift, degraded performance, invalid inputs, or use outside approved scope

• Retirement or replacement procedures when a model is obsolete or no longer suitable

How it appears in operations

In manufacturing systems, model governance may apply to forecasting models, predictive maintenance models, quality risk scoring, anomaly detection, scheduling optimization, or computer vision used in inspection. Operationally, it often shows up as approval workflows, controlled releases, audit trails, periodic review records, and links between the model and the MES, ERP, historian, QMS, or other production systems where outputs are used.

For example, if a model is used to prioritize inspections or flag process deviations, governance helps define who can change the model, what testing is required before release, how performance is checked over time, and what happens if results become unreliable.

Common confusion

Model governance is often confused with data governance, algorithm design, or MLOps. These are related but not identical:

• Data governance focuses on data quality, ownership, access, lineage, and use.

• MLOps focuses on the technical practices for building, deploying, and operating machine learning workflows.

• Software governance applies to software development and release control more broadly, whether or not models are involved.

Model governance overlaps with all three, but is specifically concerned with controlling model risk, traceability, and lifecycle decisions.

Boundary of the term

The term usually applies to analytical and AI models that influence decisions, recommendations, alerts, or automated actions. It generally does not refer to physical product models such as CAD models, nor to business operating models in the organizational sense, unless the surrounding context clearly indicates those meanings.