A machine learning engineer wants to parallelize the inference of group-specific models using the Pandas...

To parallelize the inference of group-specific models using the Pandas Function API in PySpark, you can use theapplyInPandasfunction. This function allows you to apply a Python function on each group of a DataFrame and return a DataFrame, leveraging the power of pandas UDFs (user-defined functions) for better performance.

prediction_df = ( df.groupby("device_id") .applyInPandas(apply_model, schema=apply_return_schema) )

In this code:

groupby("device_id"): Groups the DataFrame by the "device_id" column.

applyInPandas(apply_model, schema=apply_return_schema): Applies theapply_modelfunction to each group and specifies the schema of the return DataFrame.

References:

PySpark Pandas UDFs Documentation