[8.x] [ML] Trained Model: Fix start deployment with ML autoscaling and 0 active nodes (#201256) #201748
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…tive nodes (elastic#201256) ## Summary During my testing, I used the current user with all required privileges but failed to notice that, after switching to the internal` kibana_system` user, it lacked the manage_autoscaling privilege required for the `GET /_autoscaling/policy` API. As a result, the `isMlAutoscalingEnabled` flag, which we rely on in the Start Deployment modal, was always set to false. This caused a bug in scenarios with zero active ML nodes, where falling back to deriving available processors from ML limits was not possible. You can check the created deployment, it correctly identifies ML autoscaling: <img width="670" alt="image" src="https://github.com/user-attachments/assets/ff1f835e-2b90-4b73-bea8-a49da8846fbd"> Also fixes restoring vCPU levels from the API deployment params. ### Checklist Check the PR satisfies following conditions. - [x] [Unit or functional tests](https://www.elastic.co/guide/en/kibana/master/development-tests.html) were updated or added to match the most common scenarios (cherry picked from commit 9827a07)
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