Concern 298 · Working draft · editorial review in progress
Model decommissioning
Model decommissioning is the controlled retirement of a model and the services, records, and permissions that depend on it.
Understanding the concern
Turning off an endpoint can break applications that still call it. Old artifacts, credentials, or stored data may also remain after the visible service ends. A retirement plan needs to identify dependents and decide what should be preserved, migrated, or removed.
This differs from a rollback, which restores an earlier version. Decommissioning ends a particular model's operational role. Keeping enough records for legitimate accountability while removing unnecessary access and data is part of completing that process.
An example
A building closes, but subscriptions, keys, stored records, and services connected to it still need to be handled.
Illustrative unless identified as a documented case.
Words in plain English
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
The sources support lifecycle and artifact management; exact retention and migration duties depend on the organization and use.
NIST · SP 800-218A · 2024 · Official guidance
Secure Software Development Practices for Generative AI and Dual-Use Foundation Models ↗ (opens in a new tab)Adds AI-specific lifecycle practices to secure software development; guidance does not certify a particular system.
MLflow documentation · Official technical documentation
Model Registry Workflows ↗ (opens in a new tab)Describes registration, versions, aliases and deployment workflows; an alias is mutable and a registry must match actual deployment.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- NIST AI RMF (opens in a new tab)
framework
A question to discuss
Have all dependent services, access grants, and retention duties been addressed before calling the model retired?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Cooperation
Cooperation means working with others toward a shared purpose while respecting their contribution. It is not simply asking people to accept a decision already made.
Share information, roles, and credit. Include the people who do the daily work when choosing or testing a tool. Make disagreements discussable so that cooperation does not become pressure to agree.
Ask together: What can we do together that none of us can do well alone?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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