RIC APP ML Overview¶
The O-RAN SC Machine Learning (ML) Common Services provides ML tools, adapters to integrate with a radio access network (RAN) controller.
Using Acumos ML models in the RIC:
- Goal is to support ML models in non-real time and near-real time RIC usecases.
- ** quickly import an Acumos model into RIC and adapt it into as an xApp (near-real time). ** deploy Acumos models as is into non-real time (mostly on ONAP side).
- Priority is to get something working with minimal changes possible on ML models
- ** focus on performance in the later releases, since many ML models take some time to execute anyway.
- Build a standard xApp/Acumos microservice adapter
- ** deployed along with the Acumos ML model in one Kubernetes pod.
- Adapter speaks RMR protocol to RIC
- ** communicates with the Acumos ML model in the standard http / GRPC manner.
- Configuration needed for each deployment
- ** to tell adapter how to speak with Acumos ML model. ** can be auto generated using ML model protobuf definition.
- Consider writing custom RMR model runner
- ** for performance in near-real time RIC xApps in the following releases.