Terms related to machine learning · 3.3.5
machine learning
the process of tuning the parameters of a model by computational means so that the behaviour reflects data or experience
What this term settles
This is the sense the standard gives machine learning. A firm is free to use another sense, but then the difference belongs in its own glossary rather than in an assumed shared language.
Who it binds
A definition binds nobody on its own, so the force of machine learning comes only from the reader who adopts it in a policy, a contract, a data schema or an assurance argument.
What to do in your practice
Map the places machine learning appears in policy, procurement and system records. Where the word is used without the entry behind it, add the citation or replace the sentence.
Evidence an auditor or regulator asks for
- A controlled vocabulary entry for machine learning with the citation 3.3.5, an owner and a review date.An auditor can check that machine learning is controlled rather than assumed.
- A model record that names the algorithm, the training run and the parameters that changed. The row names machine learning.Shows machine learning in use in the artefact the firm relies on, not only in a glossary.
- A training log with the data version, the criteria and the result. The record carries the machine learning entry and its date.The model record names the vendor product but not the algorithm or the training run, so the term cannot be traced to an artefact. The gap is tested against machine learning.
Common gaps this page catches
- machine learning is used in the model and training record without a written definition, so two teams can mean different things.
- machine learning appears in a template with no citation, so the reader cannot tell which sense was intended.
What to prepare before the amendment lands
Brief the owners who use machine learning most often, then ask each to return one artefact that shows the entry in use. File the artefacts against the register row.
How to test it here
Take one live artefact rather than a template, find the sentence or field where machine learning is used, and put this page beside it. Record the answer, the artefact and the date, because that record is the evidence and a glossary entry on its own is only an intention.
What this page does not claim
The page does not reproduce the standard, certify the firm or predict the wording the amendment will use; it states machine learning at the claim level "definition" and stops where the public record stops.
Common questions
- What does machine learning mean in ISO 22989?
- the process of tuning the parameters of a model by computational means so that the behaviour reflects data or experience
- What evidence should we keep for 3.3.5?
- A controlled vocabulary entry for machine learning with the citation 3.3.5, an owner and a review date. A model record that names the algorithm, the training run and the parameters that changed. The row names machine learning.
- Is machine learning a requirement?
- No: machine learning is a definition or clause of a terminology standard, and it becomes binding only when a firm writes it into a policy, a contract or a procedure, or when another standard incorporates it.
Related units
Source. ISO/IEC 22989:2022, Clause 3.3.5, Terms and definitions. Open the ISO public entry. Retrieved 21 October 2026.
https://www.iso.org/obp/ui/en/#iso:std:iso-iec:22989:ed-1:v1:en
ISO catalogue record 88145, the amendment status, stage and dates. Catalogue record 74296, the base standard.
Claim level: definition. The definition is in the public preview. This page states it in our own words and does not reproduce the text.
Last updated: 2026-10-21 · Markdown version · Check your vocabulary