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Thank you! We still love Google Cloud ML engine - it's perfect for those who want to run in the cloud and want a layer of abstraction. This is for people who wa
by TheIronYuppie 9y ago
Thank you! We still love Google Cloud ML engine - it's perfect for those who want to run in the cloud and want a layer of abstraction. This is for people who want portable stacks and a bit more control; and/or want to use their Kubernetes deployments (particularly on-premise or multi-purpose).
Does that help?
Disclosure: I work at Googlr on Kubeflow
- ecnahc515 9y agoDo you foresee some kind of integration between Google Cloud ML engine and Tensorflow on k8s in the future?
- TheIronYuppie 9y agoWe're always ready to talk roadmap - anything in particular you'd like to see integration-wise? Disclosure: I work at Google on Kubeflow
- ethbro 9y agoOff the top of my head, maybe a maintained "ml-engine aligned" kubeflow setup, to the extent that's possible. The use case I'm think of is an ml dev team building on kubeflow and proving a system. Then wanting to transfer it to a non-engineering team, yet wash their hands of any ongoing infrastructure ops responsibility. Knowing that a "ml-engine aligned" kubeflow config would transfer cleanly (including associated bells and whistles) would make that a much more attractive option. Caveat: I'll admit I'm not keeping up on what's in the managed offering, but I'm assuming there are a number of value-adds of the type that end users like (visualizations, etc).
- TheIronYuppie 9y agoYes, this is EXACTLY what we're trying to do! However, it's a bit early, so I can't say when or where we'll be able to get to it. Also, I should be clear, though I'm from Google, we would really like the same story to work with other cloud's hosted offerings as well, but we'll need their support to do so! Disclosure: I work at Google on Kubeflow