I think you need to describe what use cases you intend once you've encoded the thing.
JSON's pretty generic. You can convert any pickle into JSON, but it'll still have the security and versioning issues of a pickle. You can convert to PMML and convert the XML to JSON, but it'd still be limited by what PMML can represent, and not be deserializable into a sklearn estimator. Are you archiving the model? Are you hoping to predict from the model? Are you hoping to continue training the model? Does it need to be deserializable to a sklearn estimator? To a generic estimator not in the central sklearn library?
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