Dear scikit-learn developers, I have developed a small webservice which can hold multiple scikit-learn models and serve post - json requests for prediction. A model must have model.metadata and must implement model.transform_predict(newdata). There are two examples: BostonModel, where only predict is overriden from WebModel IrisModel, where predict and transform is overriden from WebModel.
The idea is, that while fitting a model, you could have some metadata which are needed for prediction. These metadata are stored as a python dictionary. metadata could hold for example: version of model when it was created additional pandas.DataFrames needed for prediction constants needed in the predict computation metrics about the model etc. The repo can be found here: https://github.com/orgesleka/webscikit It comes with two examples: iris and boston. The server can load other models at runtime, in case one is changing the models. The repo is meant as a proof of concept. If somebody has ideas on how to improve things or adding new features, that would be great. To get started, see: https://github.com/orgesleka/webscikit/wiki/Getting-started Kind regards Orges Leka
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