Ml Functional Performance Criteria
ML functional performance criteria are predefined requirements that specify acceptable levels of performance for a machine learning model.
“Criteria based on ML functional performance metrics used as a basis for model evaluation, tuning and testing.”
(Definition reproduced from the ISTQB Glossary. Copyright belongs to ISTQB.)
View the complete ISTQB GlossaryWhat is Ml Functional Performance Criteria?
These criteria establish measurable objectives such as minimum accuracy, precision, recall, or latency that a model must satisfy before deployment.
Acceptance thresholds: Performance targets define deployment readiness.
Business alignment: Criteria reflect business and technical objectives.
Evaluation basis: Metrics are compared against established criteria.
Real World Example
A national travel service is preparing for a public holiday sale where thousands of users may search, reserve, and pay for tickets at the same time.
A feature that works for one tester may fail under traffic, resource pressure, or infrastructure disruption. Ml functional performance criteria helps the team focus on behavior under operational conditions.
The tester defines realistic traffic, failure, and recovery situations, then watches response time, errors, data consistency, and whether users can continue critical actions.
The release decision improves because Ml functional performance criteria gives stakeholders evidence about service behavior when demand or disruption is no longer theoretical.
Practice Questions
Question 1
Which statement BEST describes Ml functional performance criteria in the context of ISTQB terminology?
Question 2
A tester needs to explain Ml functional performance criteria to a non-technical stakeholder. Which approach is MOST appropriate?
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