Arbitrary MLflow models can be used with pandas-on-Spark Dataframes, provided they implement the ‘pyfunc’ flavor. This is the case for most frameworks supported by MLflow (scikit-learn, pytorch, tensorflow, …). See comprehensive examples in load_model() for more information.
load_model()
Note
The MLflow package must be installed in order to use this module.
PythonModelWrapper(model_uri, return_type_hint)
PythonModelWrapper
A wrapper around MLflow’s Python object model.
load_model(model_uri[, predict_type])
load_model
Loads an MLflow model into an wrapper that can be used both for pandas and pandas-on-Spark DataFrame.