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	Model registration to trigger feature group creation/data request to DME? Support for dynamic change of data source Trigger training only after data is ready in DB
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

Planned EPICs

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