Missing charm config to expose the --serve-artifacts option for mlflow

[Barteus]

Context

When accessing the mlflow from outside the cluster the S3 storage is not exposed, the default mlflow library in python requires flag --serve-artifacts to use tracking server to upload artefacts instead of directly uploading them to the object storage.

From mlflow documentation:

If the MLflow server is not configured with the --serve-artifacts option, the client directly pushes artifacts to the artifact store. It does not proxy these through the tracking server by default.

For data security its better to not expose the S3 storage to the world if possible.

What needs to get done

1. add config option
2. if server-artifacts == True, add flag --serve-artifacts to mlflow server execution

Definition of Done

There is a way to enable the serve-artifacts flag for MLflow.

[syncronize-issues-to-jira]

Thank you for reporting us your feedback!

The internal ticket has been created: https://warthogs.atlassian.net/browse/KF-6468.

This message was autogenerated

[mathmarchand]

I can work on this, Can I be assigned to this issue?