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load saved model pyspark

print(spark.version)
2.4.3

# fit model
cvModel = cv_grid.fit(train_df)

# save best model to specified path
mPath =  "/path/to/model/folder"
cvModel.bestModel.write().overwrite().save(mPath)

# read pickled model via pipeline api
from pyspark.ml.pipeline import PipelineModel
persistedModel = PipelineModel.load(mPath)

# predict
predictionsDF = persistedModel.transform(test_df)

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