Charmed MLFLow beta is here!

We are happy to announce that Charmed MLFlow now available in Beta. MLFlow is a foundational part of the MLOps ecosystem that has been evolving over the years. With Charmed Kubeflow 1.7, users will benefit from the ability to run serverless workloads and perform model inference regardless of the machine learning framework they use.

We’re looking for data scientists, ML engineers and developers to take the Beta release for a drive and share their feedback! Our blog is available if you want to read more.

For deploying, you can follow our guide. You can report the bugs right away in GitHub.

Please be mindful that this is not a stable version, so there is always a risk that something might go wrong. Save your work to proceed with caution. If you encounter any difficulties, Canonical’s MLOps team is here to hear your feedback and help you out. Since this is a Beta version, Canonical does not recommend running or upgrading it on any production environment.


The kubeflow Charm is documented as not being laptop friendly (I suspect it’s the wrapped kubeflow that’s the killer and not the Charm wrapper). Is MLFlow therefore laptop friendly? (This post and the linked blog suggest they are… I plan on trying it myself soon so this could simply be a disparity in what is documented).

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Thank you for reaching out. You are right, Kubeflow is heavy for an average priced laptop - it works on high end ones. However, due to its integrations, it benefits artifact migration & compatibility. It is designed mainly for AI at scale and scale of projects.

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Hello. Since feedback is at the heart of our product development, this week, on 16th of June I will be going to Open Source Festival to play a bit with Charmed MLFlow beta as part of a workshop and see what we could improve better.

If it happens that you are in Nigeria or you have friends participating at the event, don’t be shy join us and have some fun:

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The team is moving quick and we just enabled Charmed MLFlow on EKS. Would you like to give it a try?

Follow our guide: Deploying Charmed MLflow v2 to EKS and share your feedback here.

Playing with MLFlow and Notebooks? This guide is quite a good place to look at:

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