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Support multiple ML Worker connected at the same time
馃攬 Motivation
To execute models trained using different python versions and different environments. To let different data scientists work with Giskard at the same time
馃洶 Alternatives
Disconnecting an actively connected ML Worker and connecting a new one. This is a current solution but it's not great because it only allows a single active worker, so no way to run models trained in different environments at the same time.
馃搸 Additional context
In the settings admin should see all actively connected ML Workers
an active ML Worker should be assigned at a project level
鉂換uestions
Should workers have unique ids to detect if a worker that was selected as "active" for a given project isn't currently available?
馃殌 Feature Request
Support multiple ML Worker connected at the same time
馃攬 Motivation
To execute models trained using different python versions and different environments. To let different data scientists work with Giskard at the same time
馃洶 Alternatives
Disconnecting an actively connected ML Worker and connecting a new one. This is a current solution but it's not great because it only allows a single active worker, so no way to run models trained in different environments at the same time.
馃搸 Additional context
In the settings admin should see all actively connected ML Workers
an active ML Worker should be assigned at a project level
鉂換uestions
Should workers have unique ids to detect if a worker that was selected as "active" for a given project isn't currently available?
From SyncLinear.com | GSK-680
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