Passing the prediction dataframe to Serializer along with the pipeline model

I don't understand the connection between the prediction results (from a call to `model.transform()`) and serialization of a model (created from a call to `pipeline.fit()`).

Is this prediction set saved and used later when I deserialize my model as an `MLeap` pipeline? (see the code below)

```
spark_prediction = model.transform(test_data)
model.serializeToBundle(model_zip_url, spark_prediction)
mleap_pipeline = PipelineModel.deserializeFromBundle(model_zip_url)
mleap_prediction = mleap_pipeline.transform(test_data)
```
Is `mleap_prediction` actually uses the saved `spark_prediction`?

Can these two be different? the `test_data `passed to create `spark_prediction `and the `test_data `passed in the call to `mleap_pipeline.transform()`?

In the call to `serializeToBundle(`) can I just pass one single record as the `test_data`?

What is the significance of the predicted data and what does `MLeap` do with that data?

Can I serialize a model without passing any predicted data?

thanks