Databricks Certified Machine Learning Associate Practice Mode

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  - 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