- class vetiver.VetiverModel(model, model_name: str, ptype_data=None, versioned=None, description: Optional[str] = None, metadata: Optional[dict] = None, **kwargs)#
Create VetiverModel class for serving.
model – A trained model, such as an sklearn or torch model
name (string) – Model name or ID
ptype_data (pd.DataFrame, np.array) – Sample of data model should expect when it is being served
versioned – Should the model be versioned when created?
description (str) – A detailed description of the model. If omitted, a brief description will be generated.
metadata (dict) – Other details to be saved and accessed for serving
Method to make predictions from a trained model
VetiverModel can also take an initialized custom VetiverHandler as a model, for advanced use cases or non-supported model types.
- __init__(model, model_name: str, ptype_data=None, versioned=None, description: Optional[str] = None, metadata: Optional[dict] = None, **kwargs)#
__init__(model, model_name[, ptype_data, ...])
from_pin(board, name[, version])