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| Vote | ||||||
|---|---|---|---|---|---|---|
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5. Very Important 4. Important 3. Good to have 2. Not important 1. No value addition. |
Dataset
If we generate dataset in testbeds (Openstack/Kubernetes + Chaos Tools such as litmus) and use it to train the ML-Models (ex: failure prediction, anomaly detection, etc), how relevant these models will be useful for production networks of Telcos?
| Vote | ||||||
|---|---|---|---|---|---|---|
| ||||||
Yes. Relevant
No. It will not be.
Why do it with testbed data when we ( have access to production data? |
Models/Frameworks
Telcos have (for past 15-20 yrs - maybe even more) AI/ML systems solving business problems (customer churns) and/or legacy Network management (failures prediction, anomaly detection, etc.). Is it Important that the ML-models we develop (related to Telco Clouds) should work in these frameworks (of legacy systems) ?
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