28.1053GPPArtificial Intelligence/ Machine Learning (AI/ML) managementManagement and orchestrationRelease 17TS
For the purposes of the present document, the terms given in 3GPP TR 21.905  and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 .
ML entity: an entity that is either an ML model or contains an ML model and ML model related metadata, it can be managed as a single composite entity.
NOTE 1: Metadata may include e.g. the applicable runtime context for the ML model.
AI decision entity: an entity that applies a non-ML based logic for making AI decisions that can be managed as a single composite entity.
ML model: mathematical algorithm that can be "trained" by data and human expert input as examples to replicate a decision an expert would make when provided that same information.
ML model training: capabilities of an ML training function to take data, run it through an ML model, derive the associated loss and adjust the parameterization of that ML model based on the computed loss.
ML training: capabilities and associated end-to-end processes to enable an ML training function to perform /ML model training (as defined above).
NOTE 2: ML training capabilities may include interaction with other parties to collect and format the data required for training the ML model, and ML model training.
ML training function: a function with ML training capabilities; it is also referred to as MLT function.
AI/ML inference function: a function that employs an ML model and/or AI decision entity to conduct inference.
For the purposes of the present document, the abbreviations given in 3GPP TR 21.905  and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in 3GPP TR 21.905 .
SBMA Service Based Management Architecture