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Recording Link: https://zoom.us/rec/share/MjEMRMH1Bta8G--Xd2PfZ_F-DLRwtaS7fWwcIxee98L0o3d6jEbuEeA5cUdu3Zib._e7wQto0wKRVN_lB
Presentation: To be addedNA
Topics:
- O-RAN F2F meetings
- "I" Release discussion
- "I" Release
- Jira tasks added and dev sprint 4 ongoing.
- https://jira.o-ran-sc.org/secure/RapidBoard.jspa?rapidView=38&projectKey=AIMLFW
- Model management
- Generic Training pipeline
- Support dynamic change of data source
- Training service
- AIMLFW bring up in NTUST
- Discuss topics for HCLTech contribution to AIMLFW
- AD usecase
- Any other topics
- Energy savings usecase. Dataset shared. AIMLFW team to look at creating a simple prediction model.
- Contribution of AIML model monitoring feature
- Handle restart of VM or system hosting AIMLFW
Discussion points:
- Model management: SANDEEP KUMAR JAISAWAL updated that core functions of model management services are created using golang. Explained the final demo scenario. Can show an initial demo of model management services can be presented in the next meeting.
- Generic Training pipeline: Navaneethan Rajasekaran provided update offline. He was able to create the generic training pipeline and test using kubeflow adapter APIs.
- Support dynamic change of data source: This activity is completed, code is reviewed and merged.
- Training service: Not planned for I release, based on MVP-C slides presented, the training service APIs are yet to be finalized.
- AIMLFW callflows: N.K. Shankaranarayanan gave an overview of the slides prepared. the AIMLFW callflows are not complete. Will have an offline discussion to make an initial set of callflows for AIMLFW. O-RAN alliance has approved AIML workflow as a new function as part of the SMO.
2023-10-17 (Tuesday)
Recording Link: https://zoom.us/rec/share/nRcyBEOuUcid1TZsfVLLg8PrSzD5Vs3LkCQiUgkeYujvd0pfm-70jHD0DpprKigI.3OStoQDEd4-2fqos
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