A machine learning engineer has developed a random forest model using scikit-learn, logged the model...

MLflow provides a module called mlflow.pyfunc that can be used to deploy any Python model as a PySpark UDF. The mlflow.pyfunc.spark_udf function takes a Spark session and a model URI, and returns a Spark UDF that can be applied to a Spark DataFrame. The model URI can be a local path, a remote path, or a run ID with a relative path within the MLflow tracking server. The mlflow.pyfunc.spark_udf function can be used with any model that has a predict function, such as scikit-learn, xgboost, etc12

The other code blocks are incorrect because:

A. The mlflow.pyfunc.spark_udf function requires a Spark session as the first argument, not a Spark DataFrame.

B. It is possible to deploy a scikit-learn model on a Spark DataFrame using mlflow.pyfunc.spark_udf or other methods, such as broadcasting the model or using Fugue.

C. There is no module called sklearn.spark_udf. To use a scikit-learn model as a Spark UDF, mlflow.pyfunc.spark_udf or other methods should be used.

D. There is no method called spark.spark_udf. To create a Spark UDF, the spark.udf.register or pyspark.sql.functions.udf methods should be used.

References:

mlflow.pyfunc — MLflow 2.9.1 documentation

Deploy models for batch inference and prediction - Databricks