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I think that the wording of the documentation and the architecture diagram are misleading.
"Continuous Deployment (VD) pipelines manage the promotion of the model and related assets through production"
In this accelerator you are not deploying the model between the different environments, you are promoting the training pipeline. Azure ML Studio doesn't currently support sharing resources across Workspaces (though this is being rectified in https://github.com/Azure/azureml-previews/tree/main/previews/registries).
Could the documentation be updated to make it clear that you are deploying the inner loop in each of the environments.
This confusion is seen in other issues such as #36
The text was updated successfully, but these errors were encountered:
@jwgwalton Thank you for the feedback. Yes, we are aware of the issue and have started working on the registries. We plan to update the current approach soon.
Appreciate your effort in setting up this repository and the starter pipelines. They work exceptionally well and are incredibly helpful for getting started! Curious if there's an update on the remaining part, particularly regarding the ETA for registering models into registries and promoting them to higher environments
I think that the wording of the documentation and the architecture diagram are misleading.
"Continuous Deployment (VD) pipelines manage the promotion of the model and related assets through production"
In this accelerator you are not deploying the model between the different environments, you are promoting the training pipeline. Azure ML Studio doesn't currently support sharing resources across Workspaces (though this is being rectified in https://github.com/Azure/azureml-previews/tree/main/previews/registries).
Could the documentation be updated to make it clear that you are deploying the inner loop in each of the environments.
This confusion is seen in other issues such as #36
The text was updated successfully, but these errors were encountered: