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I definitely see the need for packages like this. So much of a data scientist's time is spent on feature engineering, but there are relatively few tools out the
by kmax12 8y ago
I definitely see the need for packages like this. So much of a data scientist's time is spent on feature engineering, but there are relatively few tools out there that are trying to improve that step in the process compared to tools for the modeling step.
I see this tool as a something that can help with the deployment piece of the feature engineering. As things stand, it's "easy" to package and deploy modeling code, but much harder to package up your feature engineering workflow. In part because there is no agreed upon standard for developing feature engineering pipelines.
I'd be curious how this could be combined with a library like Featuretools (http://github.com/featuretools/featuretools/ http://github.com/featuretools/featuretools/) which helps automate the discovery of features, but currently has less functionality related to deployment.
(full disclosure: I work on Featuretools)
- pplonski86 8y agoI think there is a need for deployment approaches for both: feature engineering and modeling. For example, please consider feature scaling and case when the mean of the feature is drifting. Then the feature engineering and probably ML model needs to be updated. I'm not aware of ready solutions for such problems.