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FeaturesPriorityComments

Model

deployment options

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 ServicesHigh

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

HighRequired 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 Highinstallation, code refactoring

Automated testing of AIMLFW

HighAutomated scripts to install and test all AIMLFW functions
Advanced Feature selectionMedium
  • 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
Training Pipeline abstractions
Integrated install with Non-RT RIC/ Near-RT RIC/SMO
Model Performance monitoringModel validationAdvanced Feature selectionModel servicesAdvanced retraining optionsAIMLFW installation optimizations
Medium
Integrate Non-RT RIC and Near-RT RIC AI/ML usecasesMediumNeed to check https://jira.onap.org/browse/DCAEGEN2-3067
Model validationLow
Advanced retraining optionsLow
Model Performance monitoringLow


View file
nameAIMLFW-I-Release-Plan.pptx
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