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WG Data proposal #673
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WG Data proposal #673
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incorporating from: https://www.google.com/url?q=https://github.com/kubeflow/community/pull/673%23issuecomment-1858308169&sa=D&source=docs&ust=1706009878028156&usg=AOvVaw3Ngz-lMd-KZ06OcXOO7Ilc
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When we say data/metadata what exactly do we mean here ? What would be the differences from the ML perspective ?
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I believe the intent was to make a difference between
used for instance in a Spark job/task
for example mnist dataset, iris dataset used to ML train a neural network, or using a Spark job to produce said data set from the enterprise data sources
as managed/indexed by the Model Registry
for example the author, model format, accuracy metrics, etc as resulting or intentional for a ML train, and that the engineer wants to index in Model Registry while pointing at the resulting ML model
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I think, it is good to have a short description in the charter. E.g. add examples for ML data: model, dataset, and ML Metadata: artifact location, model training metrics.
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Data Preparation
not 100% if this might be confused with Notebooks? 🤔There was a problem hiding this comment.
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@rimolive suggested to
s/Data Preparation/Big Data Processing/
or something like thatThere was a problem hiding this comment.
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refs https://www.google.com/url?q=https://github.com/kubeflow/community/blob/cd3a73a86e43bf43ceb72d1e0030f07ee1792e2a/wgs/templates/wg-charter-template.md?plain%3D1%23L48-L52&sa=D&source=docs&ust=1706009878029299&usg=AOvVaw3KMVpK7DBdrn2Gp_uZU484
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I believe I should not add subprojects belonging outside of github.com/kubeflow here, what is the @kubeflow/kubeflow-steering-committee view on this?