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| Features | Priority | Comments |
|---|---|---|
Model management and exposure (MME) service | High | Implement according to procedures/APIs defined by O-RAN alliance Reference: CMCC.AO-2023.06.02-WG2-CR-0019-R1GAP-AIML model management and exposure services-v4.docx |
| Training Services | High | Implement according to procedures/APIs defined by O-RAN alliance Reference: INT-2023.05.30-WG2-CR-00050-AIML training use cases-v03.docx |
Generic Training Pipeline | High | Required to support the about services. Create a default generic kubeflow pipeline as part of installation, which the training service can utilize based on the model information provided during training job creation. |
| AIMLFW optimizations | High | installation, code refactoring |
Automated testing of AIMLFW | High | Automated scripts to install and test all AIMLFW functions |
| Advanced Feature selection | Medium |
|
| Integrated install with Non-RT RIC/ Near-RT RIC/SMO | Medium | |
| Integrate Non-RT RIC and Near-RT RIC AI/ML usecases | Medium | Need to check https://jira.onap.org/browse/DCAEGEN2-3067 |
| Different model deployment options | Medium | Currently we expose models in the form of zip files that can be deployed. Need to check O-RAN alliance approach. |
| Model validation | Low | |
| Advanced retraining options | Low | |
| Model Performance monitoring | Low |
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Planned EPICs
- Generic Training Pipeline
- New usecases to be supported on AIMLFW
- Model management and exposure (MME) services
- Training Services
- DME Interface enhancements
- AIMLFW optimizations (installation, code refactoring)
- Automated testing of AIMLFW