Create a vetiver object for deployment of a trained modelSource:
vetiver_model() object collects the information needed to store, version,
and deploy a trained model. Once your
vetiver_model() object has been
created, you can:
vetiver_model( model, model_name, ..., description = NULL, metadata = list(), save_ptype = TRUE, versioned = NULL ) new_vetiver_model(model, model_name, description, metadata, ptype, versioned)
Model name or ID.
Other method-specific arguments passed to
vetiver_ptype()to compute an input data prototype, such as
ptype_data(a sample of training features).
A detailed description of the model. If omitted, a brief description of the model will be generated.
A list containing additional metadata to store with the pin. When retrieving the pin, this will be stored in the
userkey, to avoid potential clashes with the metadata that pins itself uses.
Should an input data prototype be stored with the model? The options are
TRUE(the default, which stores a zero-row slice of the training data),
FALSE(no input data prototype for visual documentation or checking), or a dataframe to be used for both checking at prediction time and examples in API visual documentation.
Should the model object be versioned when stored with
vetiver_pin_write()? The default,
NULL, will use the default for the
boardwhere you store the model.
An input data prototype. If
NULL, there is no checking of new data at prediction time.
You can provide your own data to
save_ptype to use as examples in the
visual documentation created by
vetiver_api(). If you do this,
consider checking that your input data prototype has the same structure
as your training data (perhaps with
hardhat::scream()) and/or simulating
data to avoid leaking PII via your deployed model.