hash stringlengths 32 32 | doc_id stringlengths 7 13 | section stringlengths 3 121 | content stringlengths 0 2.2M |
|---|---|---|---|
e341f05abb94e155b675f29c6779693c | 104 230 | 11.6.1 Overview | This use case defines how an rApp requests the deployment of an AI/ML model as an AI/ML model Consumer. |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.6.2 Background and goal of the use case | The AI/ML model deployment procedures are defined as part of the AI/ML workflow services as part of AI/ML workflow services in R1GAP [1]. |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.6.3 Entities/resources involved in the use case | 1) AI/ML workflow functions: a) support model management and exposure functionality to allow an rApp to request the deployment of an AI/ML model. 2) rApp: a) initiates the procedure to deploy the updated artifact version of an AI/ML model being consumed by the rApp. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 129 |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.6.4 Solutions | |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.6.4.1 Retrieve model | Table 11.6.4.1-1: Model deployment Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp requests to deploy an AI/ML model being consumed by the rApp. Actors and Roles - rApp in the role of Model deployment service Consumer. - AI/ML workflow functions in the role of Model deployment service Producer. Assumptions n/a Preconditions - The rApp is authorized to access the Model deployment service. - The rApp is authorized to access the AI/ML model referenced in the deployment request. - The AI/ML model referenced in the deployment request is being consumed by the rApp requesting the deployment. Begins when The rApp determines the need to deployment an AI/ML model. Step 1 (M) The rApp requests the AI/ML workflow functions to deploy an AI/ML model by providing the rAppId, the AI/ML model identifier and the target deployment location of the AI/ML model. Step 2 (M) The AI/ML workflow functions authorize the rApps deployment request. Step 3 (M) The AI/ML workflow functions send the model deployment response to the rApp. The AI/ML workflow functions trigger the rApp deployment procedure. See note. Step 4 (M) The AI/ML workflow functions notify the rApp the model deployment status. Ends when n/a Exceptions n/a Post Conditions The model deployment status is known to the rApp. Traceability REQ-R1-AIML-Deploymodel-FUN1. NOTE: The rApp deployment procedure is not specified in the present document. @startuml!pragma teoz trueskinparam ParticipantPadding 5skinparam BoxPadding 10skinparam defaultFontSize 12skinparam lifelineStrategy solid skinparam SequenceGroupBackgroundColor Transparentskinparam SequenceGroupBodyBackgroundColor Transparentautonumber box "Non-RT RIC" #whitesmoke box #ivory participant "Model Consumer rApp" as rApp endbox box " Non-anchored functions in SMO/Non-RT RIC Framework " #cadetBlue participant "AI/ML workflow functions" as ML endboxendbox group Model deploymentrApp -> ML : <<R1>> Model deployment request (rAppId, AI/ML Model Identifier,\n the target deployment location of the AI/ML model)ML -> ML: AuthZnote right Check authorization in collaboration with SME functionsend note ML -> rApp : <<R1>> Model deployment response note over ML The rApp deployment procedure is not specified in this specification end note ML -> rApp : <<R1>> Model deployment status notification (AI/ML model identifier, deployment status) @enduml ETSI ETSI TS 104 230 V10.0.0 (2026-02) 130 Figure 11.6.4.1-1: Model deployment use case flow diagram |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.6.5 Required data | For a model deployment request, the rApp provides the rAppId, AI/ML model identifier, and the target deployment location of the AI/ML model. For a model deployment status notification, AI/ML workflow functions provide AI/ML model identifier and the deployment status. 11.7 AI/ML workflow-related use case 7: AI/ML model performance monitoring - AIML workflow functions consuming performance monitoring service 11.7.1 Overview This use case enables the AI/ML workflow functions as AI/ML model performance monitoring service consumer to subscribe to AI/ML model performance and receive notifications of AI/ML model performance information. 11.7.2 Background and goal of the use case An AI/ML model performance monitoring service consumer can subscribe to AI/ML model performance and receive notifications of AI/ML model performance information. 11.7.3 Entities/resources involved in the use case 1) rApp as AI/ML model performance monitoring service Producer: a) supports the functionality to allow an authorized consumer to subscribe to, and receive notifications of, AI/ML model performance information. 2) AI/ML workflow functions as AI/ML model performance monitoring service Consumer: a) support the functionality to initiate the procedure to subscribe to, and receive notifications of, AI/ML model performance information. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 131 |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.7.4 Solutions | |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.7.4.1 Subscribe AI/ML model performance | Table 11.7.4.1-1: Subscribe AI/ML model performance Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML workflow functions subscribe to AI/ML model performance. Actors and Roles - rApp in the role of AI/ML model performance monitoring service Producer. - AI/ML workflow functions in the role of AI/ML model performance monitoring service Consumer. Assumptions - The supported AI/ML model performance information are part of the AI/ML model registration information. Preconditions - The rApp has deployed a registered AI/ML model. Begins when The AI/ML workflow functions determine the need to monitor the AI/ML model performance of a deployed AI/ML model. Step 1 (M) The AI/ML workflow functions subscribe to AI/ML model performance with AI/ML model identifier, required AI/ML model performance information (optional), periodicity (optional), event notification conditions (optional), and notification URI as parameters. Step 2 (M) The rApp validates the subscription request. Step 3 (M) The rApp creates the subscription. Step 4 (M) The rApp responds to the request with subscription ID as a parameter. Ends when The subscription is created, and AI/ML workflow functions have the subscription ID. Exceptions n/a Post Conditions n/a Traceability REQ-R1-AIML-Performancer-FUN1. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid autonumber box "Non-RT RIC" #whitesmoke box #ivory participant rApp as rApp end box box " Non-anchored functions in SMO/Non-RT RIC Framework" #cadetBlue participant "AI/ML Workflow functions" as AIML end box AIML -> rApp : <<R1>> Subscribe AI/ML model performance request (AI/ML model identifier, \n AI/ML model performance information (optional), Periodicity (optional),\n EventNotificationConditions (optional), Notification URI) rApp --> rApp : Validate request rApp --> rApp : Create subscription rApp -> AIML: <<R1>> Subscribe AI/ML model performance response (Subscription ID) @enduml ETSI ETSI TS 104 230 V10.0.0 (2026-02) 132 Figure 11.7.4.1-1: Subscribe AI/ML model performance use case flow diagram ETSI ETSI TS 104 230 V10.0.0 (2026-02) 133 |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.7.4.2 Notify AI/ML model performance | Table 11.7.4.2-1: Notify AI/ML model performance Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp notifies subscribed consumers of AI/ML model performance information. Actors and Roles - rApp in the role of AI/ML model performance monitoring service Producer. - AI/ML workflow functions in the role of AI/ML model performance monitoring service Consumer. Assumptions - The supported AI/ML model performance information are part of the AI/ML model registration information. Preconditions - The rApp has deployed a registered AI/ML model. - The AI/ML workflow functions subscribed to AI/ML model performance. Begins when The rApp determines the need to notify subscribed consumers of the performance information of a deployed AI/ML model. Alternative procedure In the case of event-based notifications, the rApp notifies subscribed consumers of the AI/ML model performance information every time an event is triggered in Step 1. Step 1 (M) The rApp checks the occurrence of the notification events specified in the subscription request. Step 2 (M) The rApp notifies the subscribed consumers of the AI/ML model performance information providing the requested AI/ML model performance information and the event triggered as parameters. If no AI/ML model performance information is specified in the subscription request, all supported AI/ML model performance information is provided. Alternative procedure In the case of periodic notifications, the rApp notifies subscribed consumers of the AI/ML model performance information as per the notification periodicity specified in the subscription. Step 3 (M) The rApp notifies the subscribed consumers of the AI/ML model performance information providing the requested AI/ML model performance information. If no AI/ML model performance information is specified in the subscription request, all supported AI/ML model performance information is provided. Ends when The rApp is able to terminate the subscription or the AI/ML model is no longer deployed. Exceptions n/a Post Conditions n/a Traceability REQ-R1-AIML-Performancer-FUN2. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid autonumber box "Non-RT RIC" #whitesmoke box #ivory participant "AI/ML model performance monitoring \n service Producer rApp" as rApp end box box " Non-anchored functions in SMO/Non-RT RIC Framework" #cadetBlue participant "AI/ML Workflow functions" as AIML end box Loop Periodicity of notifications specified in the subscription request Alt Event based notifications rApp --> rApp : Check if specified event is triggered rApp -> AIML : <<R1>> Notify AI/ML model performance (AI/ML model identifier, \n AI/ML model performance information, EventNotificationType) else Periodical notifications rApp -> AIML : <<R1>> Notify AI/ML model performance (AI/ML model identifier, \n AI/ML model performance information) end end ETSI ETSI TS 104 230 V10.0.0 (2026-02) 134 @enduml Figure 11.7.4.2-1: Notify AI/ML model performance use case flow diagram 11.7.5 Required data For the Subscribe AI/ML model performance request, the AI/ML workflow functions provide the AI/ML model identifier and notification URI. In the Subscribe AI/ML model performance request, the AI/ML workflow functions optionally provide the required AI/ML model performance information, Notification event conditions upon which the Notify AI/ML model performance is triggered, and periodicity of the notification. For the Subscribe AI/ML model performance response, the rApp provides the Subscription ID. For the Notify AI/ML model performance, the rApp provides the AI/ML model performance information specified in the Subscribe AI/ML model performance request, as well as the event that triggered the notification if notification event conditions are specified in the Subscribe AI/ML model performance request. If no required AI/ML performance information is specified in the Subscribe AI/ML model performance request, all supported AI/ML model performance information is provided in the Notify AI/ML model performance. 11.8 AI/ML workflow-related use case 8: AI/ML model performance monitoring - rApp consuming performance monitoring service |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.8.1 Overview | This use case defines how an rApp as AI/ML model performance monitoring service Consumer subscribes to AI/ML model performance and receives notifications of AI/ML model performance information. |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.8.2 Background and goal of the use case | An AI/ML model performance monitoring service Consumer can subscribe to AI/ML model performance and receive notifications of AI/ML model performance information. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 135 |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.8.3 Entities/resources involved in the use case | 1) rApp as AI/ML model performance monitoring service Producer: a) supports the functionality to allow an authorized consumer to subscribe to, and receive notifications of, AI/ML model performance information. 2) rApp as AI/ML model performance monitoring service Consumer: a) supports the functionality to initiate the procedure to subscribe to, and receive notifications of, AI/ML model performance information. |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.8.4 Solutions | |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.8.4.1 Subscribe AI/ML model performance | Table 11.8.4.1-1: Subscribe AI/ML model performance Use Case Stage Evolution / Specification <<Uses>> Related use Goal rApp subscribes to AI/ML model performance. Actors and Roles - rApp in the role of AI/ML model performance monitoring service Producer. - rApp in the role of AI/ML model performance monitoring service Consumer. Assumptions - The supported AI/ML model performance information are part of the AI/ML model registration information. Preconditions - The AI/ML model performance monitoring service producer rApp has deployed a registered AI/ML model. Begins when The AI/ML model performance monitoring service Consumer rApp determines the need to monitor the AI/ML model performance of a deployed AI/ML model. Step 1 (M) The AI/ML model performance monitoring service Consumer rApp subscribe to AI/ML model performance with AI/ML model identifier, required AI/ML model performance information (optional), periodicity (optional), event notification conditions (optional), and notification URI as parameters. Step 2 (M) The AI/ML model performance monitoring service Producer rApp checks with SME functions whether the AI/ML model performance monitoring service Consumer is authorized to subscribe to AI/ML model performance. Step 3 (M) The AI/ML model performance monitoring service Producer rApp validates the subscription request. Step 4 (M) The rApp creates the subscription. Step 5 (M) The rApp responds to the request with subscription ID as a parameter. Ends when The subscription is created, and The AI/ML model performance monitoring service Consumer rApp has the subscription ID. Exceptions n/a Post Conditions n/a Traceability REQ-R1-AIML-Performancer-FUN1. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid autonumber box "Non-RT RIC" #whitesmoke box #ivory participant "The AI/ML model performance monitoring \n service Producer rApp" as rApp participant " The AI/ML model performance monitoring \n service Consumer rApp " as AIML end box box " Non-anchored functions in SMO/Non-RT RIC Framework" #cadetBlue participant "SME functions" as SME ETSI ETSI TS 104 230 V10.0.0 (2026-02) 136 end box AIML -> rApp : Subscribe AI/ML model performance request (AI/ML model identifier, \n AI/ML model performance information (optional), Periodicity (optional),\n EventNotificationConditions (optional), Notification URI) rApp --> SME : <<R1>> AuthZ note right Check authorization in collaboration with SME functions end note rApp --> rApp : Validate request rApp --> rApp : Create subscription rApp -> AIML: Subscribe AI/ML model performance response (Subscription ID) @enduml Figure 11.8.4.1-1: Subscribe AI/ML model performance use case flow diagram ETSI ETSI TS 104 230 V10.0.0 (2026-02) 137 |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.8.4.2 Notify AI/ML model performance | Table 11.8.4.2-1: Notify AI/ML model performance Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp notifies subscribed consumers of AI/ML model performance information. Actors and Roles - rApp in the role of AI/ML model performance monitoring service Producer. - rApp in the role of AI/ML model performance monitoring service Consumer. Assumptions - The supported AI/ML model performance information are part of the AI/ML model registration information. Preconditions - The rApp has deployed a registered AI/ML model. - The AI/ML model performance monitoring service consumer rApp subscribed to AI/ML model performance. Begins when The rApp determine the need to notify subscribed consumers of the performance information of a deployed AI/ML model. Alternative procedure In the case of event-based notifications, the rApp notifies subscribed consumers of the AI/ML model performance information every time an event is triggered in Step 1. Step 1 (M) The rApp checks the occurrence of the notification events specified in the subscription request. Step 2 (M) The rApp notifies the subscribed consumers of the AI/ML model performance information providing the requested AI/ML model performance information and the event triggered as parameters. If no AI/ML model performance information is specified in the subscription request, all supported AI/ML model performance information is provided. Alternative procedure In the case of periodic notifications, the rApp notifies subscribed consumers of the AI/ML model performance information as per the notification periodicity specified in the subscription. Step 3 (M) The rApp notifies the subscribed consumers of the AI/ML model performance information providing the requested AI/ML model performance information. If no AI/ML model performance information is specified in the subscription request, all supported AI/ML model performance information is provided. Ends when The rApp is able to terminate the subscription or the AI/ML model is no longer deployed. Exceptions n/a Post Conditions n/a Traceability REQ-R1-AIML-Performancer-FUN2. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid autonumber box "Non-RT RIC" #whitesmoke box #ivory participant "AI/ML model performance monitoring \n service Producer rApp" as rApp participant " The AI/ML model performance monitoring \n service Consumer rApp " as AIML end box Loop Periodicity of notifications specified in the subscription request Alt Event based notifications rApp --> rApp : Check if specified event is triggered rApp -> AIML : Notify AI/ML model performance (AI/ML model identifier, \n AI/ML model performance information,EventNotificationType) else Periodical notifications rApp -> AIML : Notify AI/ML model performance (AI/ML model identifier, \n AI/ML model performance information) end end @enduml ETSI ETSI TS 104 230 V10.0.0 (2026-02) 138 Figure 11.8.4.2-1: Notify AI/ML model performance use case flow diagram |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.8.5 Required data | For the Subscribe AI/ML model performance request, the AI/ML model performance monitoring service Consumer rApp provides the AI/ML model identifier and notification URI. In the Subscribe AI/ML model performance request, the AI/ML model performance monitoring service Consumer rApp optionally provides the required AI/ML model performance information, Notification event conditions upon which the Notify AI/ML model performance is triggered, and periodicity of the notification. For the Subscribe AI/ML model performance response, the AI/ML model performance monitoring service Producer rApp provides the Subscription ID. For the Notify AI/ML model performance, the AI/ML model performance monitoring service producer rApp provides the AI/ML model performance information specified in the Subscribe AI/ML model performance request, as well as the event that triggered the notification if notification event conditions are specified in the Subscribe AI/ML model performance request. If no required AI/ML performance information is specified in the Subscribe AI/ML model performance request, all supported AI/ML model performance information is provided in the Notify AI/ML model performance. 11.9 AI/ML workflow-related use case 9: AI/ML model training capability registration and deregistration |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.9.1 Overview | This use case defines how an rApp registers and deregisters AI/ML model training capability. |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.9.2 Background and goal of the use case | The register AI/ML model training capability and deregister AI/ML model training capability are optional procedures defined as part of the AI/ML model training capability registration service in R1GAP [1]. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 139 |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.9.3 Entities/resources involved in the use case | 1) AI/ML workflow functions: a) support functionality to allow an rApp to register and deregister AI/ML model training capability; b) support validation of AI/ML model training capability information. 2) rApp: a) initiates the procedure to register and deregister AI/ML model training capability. |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.9.4 Solutions | |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.9.4.1 Register AI/ML model training capability | Table 11.9.4.1-1: AI/ML Model training capability registration Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp registers an AI/ML model training capability. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the role of AI/ML model management and exposure service Producer. Assumptions n/a Preconditions - The rApp is authorized to access the AI/ML model management and exposure services. - The rApp is registered as producer of AI/ML model training with SME, it intends to register its training capability in AI/ML workflow functions. Begins when The rApp determines the need to register an AI/ML model training capability. Step 1 (M) The rApp requests the AI/ML workflow functions to register an AI/ML model training capability by providing the rAppId and AI/ML model training capability information. Step 2 (M) The AI/ML workflow functions check with SME functions whether the rApp is registered as producer of AI/ML model training service. Step 3 (M) The AI/ML workflow functions register the rApp's AI/ML model training capability. Step 4 (M) The AI/ML workflow functions respond to rApp with successful AI/ML model training capability registration along with AI/ML model training capability registration ID. Ends when The rApp was able to register an AI/ML model training capability. Exceptions n/a Post Conditions The AI/ML model training capability is registered. The rApp can query, update or delete the AI/ML model training capability registration. Traceability REQ-R1-AI/ML-Registertraincap-FUN1. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid skinparam SequenceGroupBackgroundColor Transparent skinparam SequenceGroupBodyBackgroundColor Transparent autonumber box "Non-RT RIC" #whitesmoke box " Non-anchored functions in SMO/Non-RT RIC Framework " #cadetBlue participant "AI/ML workflow functions" as ML endbox box #ivory participant "Training service Producer rApp" as src ETSI ETSI TS 104 230 V10.0.0 (2026-02) 140 endbox endbox src -> ML: <<R1>> Register AI/ML model training capability request (rAppId, AI/ML model training capability information) ML -> ML: Authz ML -> ML: register AI/ML model training capability ML -> src: <<R1>> Register AI/ML model training capability response (AI/ML model training capability registration ID) @enduml Figure 11.9.4.1-1: Register AI/ML model training capability use case flow diagram |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.9.4.2 Deregister AI/ML model training capability | Table 11.9.4.2-1: AI/ML Model training capability deregistration Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp deregisters an AI/ML model training capability. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the role of AI/ML model management and exposure service Producer. Assumptions The rApp is not producing any AI/ML model training capability. Preconditions The rApp has registered an AI/ML model training capability. The rApp is authorized to access the AI/ML model management and exposure services. Begins when The rApp determines the need to deregister an AI/ML model training capability that it no longer intends to produce. Step 1 (M) The rApp requests AI/ML workflow functions to deregister an AI/ML model training capability by providing rAppId and AI/ML model training capability registration ID. Step 2 (M) The AI/ML workflow functions remove the registration of the rApp as producer of AI/ML model training capability. Step 3 (M) The AI/ML workflow functions respond to the rApp with successful deregistration. Ends when The rApp was able to deregister the AI/ML model training capability. Exceptions n/a Post Conditions n/a Traceability REQ-R1-AI/ML-Registertraincap-FUN1. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid skinparam SequenceGroupBackgroundColor Transparent skinparam SequenceGroupBodyBackgroundColor Transparent autonumber box "Non-RT RIC" #whitesmoke box " Non-anchored functions in SMO/Non-RT RIC Framework " #cadetBlue participant "AI/ML workflow functions" as ML ETSI ETSI TS 104 230 V10.0.0 (2026-02) 141 endbox box #ivory participant "Training service Producer rApp" as src endbox endbox src -> ML: <<R1>> Deregister AI/ML model training capability (rAppId, AI/ML model training capability registration ID) ML -> ML: Deregister AI/ML model training capability ML -> src: <<R1>> Deregister AI/ML model training capability response @enduml Figure 11.9.4.2-1: Deregister AI/ML model training capability use case flow diagram |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.9.4.3 Update AI/ML model training capability registration | Table 11.9.4.3-1: Update AI/ML model training capability registration Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp updates the registration of an AI/ML model training capability. Actors and Roles - rApp in the role of AI/ML model management and exposure services Consumer. - AI/ML workflow functions in the role of AI/ML model management and exposure services Producer. Assumptions n/a Preconditions - The rApp is authorized to access the AI/ML model management and exposure services for updating an AI/ML model training capability registration. - The rApp has registered an AI/ML model training capability. Begins when The AI/ML model training service producer rApp determines the need to update the registration of an AI/ML model training capability. Step 1 (M) The AI/ML model training service producer rApp requests to update the registration of an AI/ML model training capability by providing rAppId, AI/ML model training capability registration ID, updated model training capability registration information, etc. Step 2 (M) The AI/ML workflow functions check whether the rApp is authorized to update the registration. Step 3 (M) The AI/ML workflow functions validate the update AI/ML model training capability registration request. Step 4 (M) The AI/ML workflow functions update the registration of the AI/ML model training capability. Step 5 (M) The AI/ML workflow functions respond to the rApp with successful update AI/ML model training capability registration response. Ends when The rApp was able to update the registration of the AI/ML model training capability. Exceptions n/a Post Conditions The rApp can query, update or delete the AI/ML model training capability registration. Traceability REQ-R1-AI/ML-Registertraincap-FUN1. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 142 @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid skinparam SequenceGroupBackgroundColor Transparent skinparam SequenceGroupBodyBackgroundColor Transparent box "Non-RT RIC" #whitesmoke box #ivory participant "rApp" as app endbox box "Non-anchored functions \n in SMO/Non-RT RIC FWK" #cadetBlue participant "AI/ML workflow functions" as aif endbox endbox autonumber app -> aif: <<R1>> Update model training capability registration request \n (rAppId, AI/ML model training capability registration identifier, \n updated model training capability registration information) activate aif aif --> aif: AuthZ note right Check authorization in collaboration with SME functions end note aif --> aif: Validate aif --> aif: Update model training capability\n registration information app <- aif: <<R1>> Update model training capability registration response deactivate aif @enduml Figure 11.9.4.3-1: Update registration of an AI/ML training capability model use case flow diagram ETSI ETSI TS 104 230 V10.0.0 (2026-02) 143 |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.9.4.4 Query AI/ML model training capability registration | Table 11.9.4.4-1: Query AI/ML model training capability registration Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp queries an AI/ML model training capability registration. Actors and Roles - rApp in the role of AI/ML model management and exposure services Consumer. - AI/ML workflow functions in the role of AI/ML model management and exposure services Producer. Assumptions n/a Preconditions - The rApp is authorized to access the AI/ML model management and exposure services for registering an AI/ML model. - The AI/ML model training capability has been registered by the rApp. Begins when The AI/ML model training service producer rApp determines the need to query the registration of a registered AI/ML model training capability. Step 1 (M) The AI/ML model training service producer rApp requests to query the registration of an AI/ML model training capability by providing rAppId and AI/ML model training capability registration ID. Step 2 (M) The AI/ML workflow functions check whether the rApp is authorized to query the AI/ML model training capability registration. Step 3 (M) The AI/ML workflow functions look up the information of queried AI/ML model training capability. Step 4 (M) The AI/ML workflow functions respond to rApp with AI/ML model training capability information. Ends when The AI/ML model training service producer rApp was able to query the AI/ML model training capability registration. Exceptions n/a Post Conditions The rApp can query, update, or delete the AI/ML model training capability registration. Traceability REQ-R1-AI/ML-Registertraincap-FUN1. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid skinparam SequenceGroupBackgroundColor Transparent skinparam SequenceGroupBodyBackgroundColor Transparent autonumber box "Non-RT RIC" #whitesmoke box #ivory participant "rApp" as src endbox box " Non-anchored functions in SMO/Non-RT RIC Framework " #cadetBlue participant "AI/ML workflow functions" as ML endbox endbox src -> ML: <<R1>> Query AI/ML model training capability request (rAppId, AI/ML model training capability registration ID) ML -> ML: Authz note right Check authorization in collaboration with SME functions end note ML -> ML: Look up AI/ML model training capability registration ML -> src: <<R1>> Query AI/ML model training capability response (AI/ML model training capability information) @enduml ETSI ETSI TS 104 230 V10.0.0 (2026-02) 144 Figure 11.9.4.4-1: Update registration of an AI/ML training capability model use case flow diagram |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.9.5 Required data | The AI/ML model training capability information includes but not limited to supported training frameworks, supported library packages, supported library versions, resource information. For registering an AI/ML model training capability, the rApp provides the rAppId and AI/ML model training capability information. On successful registration, AI/ML workflow functions provide an AI/ML model training capability registration ID for the registration of the AI/ML model training capability information. For deregistering an AI/ML model training capability, the rApp provides the rAppId and the AI/ML model training capability registration ID. For updating the registration of an AI/ML model training capability, the rApp needs to provide the rAppId, the AI/ML model training capability registration ID and the updated AI/ML model training capability information or the modified part of the AI/ML model training capability information. For querying the registration of an AI/ML model training capability, the rApp needs to provide the rAppId and the AI/ML model training capability registration ID. 11.10 AI/ML workflow-related use case 10: AI/ML model inference - AI/ML workflow functions producing AI/ML inference 11.10.1 Overview This use case enables an rApp to request and cancel an inference for an AI/ML model. 11.10.2 Background and goal of the use case The request and cancel AI/ML model inference procedures are defined as part of the AI/ML workflow services in R1GAP [1]. 11.10.3 Entities/resources involved in the use case 1) AI/ML workflow functions in the role of AI/ML model inference service Producer: a) support functionality allowing rApps to request, and cancel the inference of a registered AI/ML Model. 2) rApp in the role of AI/ML model inference service Consumer: a) initiates the procedure to request and cancel the inference of an AI/ML model. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 145 11.10.4 Solutions |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.10.4.1 Request AI/ML model inference | Table 11.10.4.1-1: Request AI/ML model inference use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML inference services Consumer requests inference for an AI/ML model. Actors and Roles - AI/ML inference services Consumer in the role of Service Consumer. - The AI/ML workflow functions in the role of Service Producer. Assumptions n/a Preconditions - The AI/ML inference services Consumer is deployed, authenticated, and authorized to consume AI/ML inference services. Begins when The AI/ML inference services Consumer determines the need to initiate the inference an AI/ML model. Step 1 (M) The AI/ML inference services Consumer requests the AI/ML workflow functions to infer an AI/ML model providing rAppId and model identifier. Step 2 (M) The AI/ML workflow functions check with SME functions whether the AI/ML inference services Consumer is authorized to request inference for an AI/ML model. Step 3 (M) The AI/ML workflow functions validate the request. Step 4 (M) The AI/ML workflow functions respond to the AI/ML inference services Consumer with inference job identifier as a parameter. Ends when The AI/ML inference services Consumer is able to obtain the inference job identifier. Exceptions n/a Post Conditions The inference job exists, and the inference service Consumer can cancel the AI/ML model inference. Traceability REQ-R1-AIML-inference-FUN1. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid skinparam SequenceGroupBackgroundColor Transparent skinparam SequenceGroupBodyBackgroundColor Transparent autonumber box "Non-RT RIC" #whitesmoke box #ivory participant "rApp" as rApp endbox box " Non-anchored functions in SMO/Non-RT RIC Framework " #cadetBlue participant "AI/ML workflow functions" as ML endbox endbox rApp -> ML: <<R1>> Request AI/ML model inference request \n (rAppId, AI/ML model identifier) ML -> ML: AuthZ note right Check authorization in Collaboration with SME functions end note ML-> ML :validate ML -> rApp: <<R1>> Request AI/ML model inference response \n (inference job identifier) @enduml ETSI ETSI TS 104 230 V10.0.0 (2026-02) 146 Figure 11.10.4.1-1: Request AI/ML model inference use case flow diagram |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.10.4.2 Cancel AI/ML model inference | Table 11.10.4.2-1: Cancel AI/ML model inference use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML inference services Consumer cancels the AI/ML model inference that it has previously requested. Actors and Roles - AI/ML inference services Consumer in the role of Service Consumer. - The AI/ML workflow functions in the role of Service Producer. Assumptions n/a Preconditions - The AI/ML inference services Consumer is deployed, authenticated, and authorized to consume AI/ML inference services. - The inference job exists, and the AI/ML inference Consumer is aware of the job identifier. Begins when The AI/ML inference services Consumer determines the need to cancel the inference of an AI/ML model. Step 1 (M) The AI/ML inference services Consumer requests the AI/ML workflow functions to cancel the inference for an AI/ML model providing rAppId and inference job identifier. Step 2 (M) The AI/ML workflow functions check with SME functions whether the AI/ML inference services Consumer is authorized to cancel the inference job for an AI/ML model. Step 3 (M) The AI/ML workflow functions cancel the inference job. Step 4 (M) The AI/ML workflow functions respond to the AI/ML inference services Consumer rApp with cancellation of inference job. Ends when The AI/ML inference service job has been cancelled. Exceptions n/a Post Conditions n/a Traceability REQ-R1-AIML-inference-FUN1. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid skinparam SequenceGroupBackgroundColor Transparent skinparam SequenceGroupBodyBackgroundColor Transparent autonumber ETSI ETSI TS 104 230 V10.0.0 (2026-02) 147 box "Non-RT RIC" #whitesmoke box #ivory participant "rApp" as rApp endbox box " Non-anchored functions in SMO/Non-RT RIC Framework " #cadetBlue participant "AI/ML workflow functions" as ML endbox endbox rApp -> ML: <<R1>> Cancel AI/ML model inference request \n (rAppId,inference job identifier) ML -> ML: AuthZ note right Check authorization in Collaboration with SME functions end note ML -> ML : Stop inference job ML -> rApp: <<R1>> Cancel AI/ML model inference response @enduml Figure 11.10.4.2-1: Cancel AI/ML model inference use case flow diagram 11.10.5 Required data For creating AI/ML model inference job, the AI/ML inference services Consumer needs to provide its rAppId and the model identifier. The AI/ML workflow functions respond with the creation of inference job by providing the inference job identifier. For cancellation of an AI/ML inference job, the AI/ML inference services Consumer needs to provide its rAppId and the inference job identifier. 11.11 AI/ML workflow-related use case 11: AI/ML model change subscription 11.11.1 Overview This use case defines how an rApp subscribes to registered AI/ML models. It enables an rApp to subscribe to and unsubscribe from notifications regarding changes in the registered AI/ML models. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 148 11.11.2 Background and goal of the use case The subscribe AI/ML models changes procedure, unsubscribe AI/ML models changes procedure and notify AI/ML models changes procedure are defined as part of the AI/ML workflow services in R1GAP [1]. 11.11.3 Entities/resources involved in the use case 1) AI/ML workflow functions: a) support functionality to allow an rApp to subscribe and unsubscribe the registered AI/ML model changes; b) support functionality to notify rApp about changes of registered AI/ML model; c) support validation of selection criteria. 2) rApp: a) initiates the procedure to subscribe and unsubscribe registered AI/ML model changes; b) support functionality to receive notification regarding changes of subscribed AI/ML model. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 149 11.11.4 Solutions |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.11.4.1 Subscribe AI/ML model changes | Table 11.11.4.1-1: Subscribe AI/ML model changes Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp subscribes to notifications regarding changes of AI/ML models. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the role of AI/ML model management and exposure service Producer. Assumptions n/a Preconditions - The rApp is authorized to access the AI/ML model management and exposure service. - The rApp is aware of the AI/ML model identifier of the AI/ML model. Begins when The rApp determines the need to subscribe to notifications regarding changes of registered AI/ML models. Step 1 (M) The rApp requests the AI/ML workflow functions to subscribe to notifications regarding AI/ML model changes with rAppId and AI/ML model identifier selection criteria. Step 2 (M) The AI/ML workflow functions receive the AI/ML model subscription request, and check whether the rApp is authorized to subscribe to identified AI/ML models. Step 3 (M) The AI/ML workflow functions validate the subscribe AI/ML model request. Step 4 (M) The AI/ML workflow functions establish the AI/ML model subscription. Step 5 (M) The AI/ML workflow functions send back a response with a subscription identifier matching the request in Step 1 to rApp. Ends when The rApp was able to subscribe to notifications regarding AI/ML model changes. Exceptions n/a Post Conditions - The rApp can receive notifications when changes are made to any of the AI/ML models that matches AI/ML model identifier selection criteria. - The rApp can unsubscribe from the notifications. Traceability n/a NOTE: AI/ML model identifier includes model name, model version and artifact version. AI/ML model identifier selection criteria could be upper and/or lower bounds of model version and artifact version number, or list of individual model version and artifact version numbers, etc. Selection criteria is used to identify AI/ML models to be subscribed, which can be applied to part, or all of AI/ML model identifier components. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid skinparam SequenceGroupBackgroundColor Transparent skinparam SequenceGroupBodyBackgroundColor Transparent autonumber box "Non-RT RIC" #whitesmoke box #ivory participant "rApp" as rApp endbox box " Non-anchored functions in SMO/Non-RT RIC Framework " #cadetBlue participant "AI/ML workflow functions" as ML endbox endbox rApp -> ML: <<R1>> Subscribe AI/ML model change request (rAppId, \n AI/ML model identifier selection criteria) ML -> ML: Authz note right Check authorization in collaboration with SME functions ETSI ETSI TS 104 230 V10.0.0 (2026-02) 150 end note ML -> ML: Validate ML -> ML: Create subscription ML -> rApp: <<R1>> Subscribe AI/ML model change response (subscription identifier) @enduml Figure 11.11.4.1-1: Subscribe AI/ML model changes use case flow diagram |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.11.4.2 Notify AI/ML model changes | Table 11.11.4.2-1: Notify AI/ML model changes Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp receives a notification regarding a change of a registered AI/ML model. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the role of AI/ML model management and exposure service Producer. Assumptions n/a Preconditions - The rApp is authorized to access the AI/ML model management and exposure service. - The rApp is subscribed to notifications. Begins when The AI/ML workflow functions determine to send a notification regarding the change of a subscribed AI/ML model to the subscribing rApp. Step 1 (M) The AI/ML workflow functions send a notification to the subscribing rApp with AI/ML model identifier and available change details. Ends when The rApp was able to receive the notification. Exceptions n/a Post Conditions n/a Traceability n/a @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid skinparam SequenceGroupBackgroundColor Transparent skinparam SequenceGroupBodyBackgroundColor Transparent autonumber box "Non-RT RIC" #whitesmoke box #ivory participant "rApp" as rApp endbox ETSI ETSI TS 104 230 V10.0.0 (2026-02) 151 box " Non-anchored functions in SMO/Non-RT RIC Framework " #cadetBlue participant "AI/ML workflow functions" as ML endbox endbox ML -> rApp: <<R1>> Notify AI/ML model changes (AI/ML model identifier, available change details) @enduml Figure 11.11.4.2-1: Notify AI/ML model changes use case flow diagram |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.11.4.3 Unsubscribe AI/ML model changes | Table 11.11.4.3-1: Unsubscribe AI/ML model changes Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp unsubscribes from notifications. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the role of AI/ML model management and exposure service Producer. Assumptions n/a Preconditions - The rApp is authorized to access the AI/ML model management and exposure service. - The rApp is subscribed to notifications regarding AI/ML model changes. Begins when The rApp determines the need to unsubscribe from notifications regarding changes of registered AI/ML model. Step 1 (M) The rApp requests the AI/ML workflow functions to unsubscribe from notifications regarding AI/ML model changes by providing the rAppId and subscription identifier. Step 2 (M) The AI/ML workflow functions check whether the rApp is authorized to unsubscribe from notifications regarding AI/ML model changes. Step 3 (M) The AI/ML workflow functions cancel the subscription. Step 4 (M) The AI/ML workflow functions respond to the request. Ends when The rApp was able to unsubscribe from notifications regarding changes of registered AI/ML model. Exceptions n/a Post Conditions n/a Traceability n/a @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid skinparam SequenceGroupBackgroundColor Transparent skinparam SequenceGroupBodyBackgroundColor Transparent autonumber box "Non-RT RIC" #whitesmoke box #ivory participant "rApp" as rApp endbox box " Non-anchored functions in SMO/Non-RT RIC Framework " #cadetBlue participant "AI/ML workflow functions" as ML endbox endbox ETSI ETSI TS 104 230 V10.0.0 (2026-02) 152 rApp -> ML: <<R1>> Unsubscribe AI/ML model change request (rAppId, subscription identifier) ML -> ML: Authz note right Check authorization in collaboration with SME functions end note ML -> ML: Cancel subscription ML -> rApp: <<R1>> Unsubscribe AI/ML model change response @enduml Figure 11.11.4.3-1: Unsubscribe AI/ML model changes use case flow diagram 11.11.5 Required data For subscription to notifications regarding changes of registered AI/ML model, the rApp provides the rAppId and the AI/ML model identifier selection criteria. The AI/ML workflow functions send back a subscription identifier in response. The AI/ML workflow functions send notifications to the subscribing rApp by providing the AI/ML model identifier(s) and available change details of AI/ML model(s). For unsubscribing from notifications regarding changes of registered AI/ML model, the rApp needs to send the rAppId and AI/ML model subscription identifier. 11.12 AI/ML workflow-related use case 12: Query of AI/ML Model inference capabilities - AI/ML workflow functions producing AI/ML inference 11.12.1 Overview This use case enables an rApp to query the inference capability information of an AI/ML model. 11.12.2 Background and goal of the use case The query AI/ML model inference capabilities procedure are defined as part of the AI/ML workflow services in R1GAP [1]. 11.12.3 Entities/resources involved in the use case 1) AI/ML workflow functions in the role of AI/ML model inference service Producer: a) Support functionality allowing rApps to query the inference capability of an AI/ML Model. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 153 2) rApp in the role of AI/ML model inference service Consumer: a) Initiates the procedure to query the inference capability of an AI/ML model. 11.12.4 Solutions |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.12.4.1 Query AI/ML model inference capability | Table 11.12.4.1-1: Query AI/ML model inference capability use case Use Case Stage Evolution / Specification <<Uses>> Related use Goal The AI/ML inference services Consumer retrieves the AI/ML model inference capability information. Actors and Roles - AI/ML inference services Consumer in the role of Service Consumer. - The AI/ML workflow functions in the role of Service Producer. Assumptions Service Consumer is authorized to request inference capability information of a registered AI/ML model. Preconditions - The AI/ML inference services Consumer is deployed, authenticated, and authorized to consume AI/ML inference services. Begins when The AI/ML inference services Consumer determines the need to query the inference capability information of a registered AI/ML model. Step 1 (M) The AI/ML inference services Consumer requests the AI/ML workflow functions to provide inference capability information by passing on the query parameters such as model identifier and associated model information. Step 2 (M) The AI/ML workflow functions check with SME functions whether the AI/ML inference services Consumer is authorized to request the inference capability information for a registered AI/ML model. Step 3 (M) The AI/ML workflow functions respond to the AI/ML inference services Consumer rApp with the requested inference capability information that matches the query criteria. Ends when The AI/ML model inference capability information has been retrieved. Exceptions n/a Post Conditions n/a Traceability REQ-R1-AIML-inference-FUN2. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid skinparam SequenceGroupBackgroundColor Transparent skinparam SequenceGroupBodyBackgroundColor Transparent autonumber box "Non-RT RIC" #whitesmoke box #ivory participant "rApp" as rApp endbox box " Non-anchored functions in SMO/Non-RT RIC Framework " #cadetBlue participant "AI/ML workflow functions" as ML endbox endbox rApp -> ML: <<R1>> Query AI/ML model inference capability request \n (rAppId, query criteria) ML -> ML: AuthZ note right Check authorization in Collaboration with SME functions end note ML -> rApp: <<R1>> Query AI/ML model inference capability response \n (inference capability information) @enduml ETSI ETSI TS 104 230 V10.0.0 (2026-02) 154 Figure 11.12.4.1-1: Query AI/ML model inference capability use case flow diagram 11.12.5 Required data For querying of an AI/ML model inference capability information, the AI/ML inference services Consumer needs to provide its rAppId and the query criteria (including the model identifier and model information). The AI/ML workflow functions respond with inference capability information such as model identifier or inference latency. 11.13 AI/ML Workflow-related use case 13: Query of AI/ML Model training capability 11.13.1 Overview This use case defines how an rApp queries AI/ML model training capability. 11.13.2 Background and goal of the use case The query AI/ML model training capability is an optional procedure defined as part of the AI/ML model training capability query service in R1GAP [1]. 11.13.3 Entities/resources involved in the use case 1) AI/ML workflow functions in the role of AI/ML model management and exposure service producer: a) Supports functionality allowing rApps to query the registered AI/ML model training capability; 2) rApp in the role of AI/ML model management and exposure service consumer: a) Initiates the procedure to query the registered AI/ML model training capability. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 155 11.13.4 Solutions |
e341f05abb94e155b675f29c6779693c | 104 230 | 11.13.4.1 Query AI/ML model training capability | Table 11.13.4.1-1: Query AI/ML model training capability. Use Case Stage Evolution / Specification <<Uses>> Related use Goal The rApp queries registered AI/ML model training capability. Actors and Roles - rApp in the role of AI/ML model management and exposure service Consumer. - AI/ML workflow functions in the role of AI/ML model management and exposure service Producer. Assumptions n/a Preconditions The rApp is authorized to access the AI/ML workflow services. At least one AI/ML model training capability is registered with the AI/ML workflow functions. Begins when The rApp determines the need to query the registered AI/ML model training capability. Step 1 (M) The rApp requests the AI/ML workflow functions for the information on the AI/ML model training capability by providing rAppId and selection criteria. Step 2 (M) The AI/ML workflow functions check if the rApp is authorized to query the registered AI/ML model training capability. Step 3 (M) The AI/ML workflow functions look up the information of queried AI/ML model training capability. Step 4 (M) The AI/ML workflow functions provide the information about the registered AI/ML model training capability. Ends when The AI/ML model training capability has been received. Exceptions n/a Post Conditions n/a Traceability REQ-R1-AI/ML-Registertraincap-FUN1. @startuml !pragma teoz true skinparam ParticipantPadding 5 skinparam BoxPadding 10 skinparam defaultFontSize 12 skinparam lifelineStrategy solid autonumber box "Non-RT RIC" #whitesmoke box #ivory participant rApp as rApp endbox box " Non-anchored functions in SMO/Non-RT RIC Framework" #cadetBlue participant " AI/ML workflow functions " as aif endbox rApp ->aif:<<R1>> Query AI/ML model training capability request\n(rAppId, Selection criteria) activate aif aif --> aif:AuthZ note right Check authorization in collaboration with SME functions end note aif --> aif: Look up AI/ML model training capability registration aif -> rApp :<<R1>> Query AI/ML model training capability response\n(AI/ML model Trainer rAppIds, AI/ML model training capability information) deactivate aif @enduml ETSI ETSI TS 104 230 V10.0.0 (2026-02) 156 Figure 11.13.4.1-1: Query AI/ML model training capability use case flow diagram 11.13.5 Required data For the Query AI/ML model training capability request, the rApp provides the rAppId and selection criteria. The AI/ML workflow functions respond with information which includes the AI/ML model Trainer rAppIds and AI/ML model training capability information that match the filtering criteria. The AI/ML model training capability information contains training platform information and training resource information. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 157 Annex A (informative): Change history Date Version Information about changes 13.03.2025 V10.00 Added use cases for AI/ML model training capability registration and data job status. 21.11.2024 V09.00 Added use cases for AIML inference capability information Query. 18.07.2024 V08.00 Added use cases for AI/ML model training capability registration, Query for data subscription, AI/ML model inference use case for create and delete and updated the SME subscription use case. 20.04.2024 V07.00 Added the use cases for AI/ML performance monitoring use cases and updated the document with Producer and Consumer roles. 20.11.2023 V06.00 Added the use cases and requirements to Data management and exposure services, A1 related services, AI/ML model workflow services, updated the data type to DME type. 29.07.2023 V05.00 Published as Final version 05.00. 24.03.2023 V04.00 Published as Final Version 04.00. 10.11.2022 V03.00 Published as Final Version 03.00. 29.07.2022 V02.00 Published as Final version 02.00. 01.04.2022 V01.00 Published as Final version 01.00. ETSI ETSI TS 104 230 V10.0.0 (2026-02) 158 History Version Date Status V10.0.0 February 2026 Publication |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.