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A machine learning engineer wants to parallelize the inference of group-specific models using the Pandas Function API. They have developed the apply_model function that will look up and load the correct model for each group, and they want to apply it to each group of DataFrame df. They have written the following incomplete code block: Which piece of code can be used to fill in the above blank to complete the task?
Correct Answer: A
To parallelize the inference of group-specific models using the Pandas Function API in PySpark, you can use the applyInPandas function. 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 the apply_model function to each group and specifies the schema of the return DataFrame. Reference: PySpark Pandas UDFs Documentation
