A machine learning engineer needs to include a custom helper module `preprocessing_utils.py` when logging a scikit-learn model to MLflow for serving. Which `mlflow.sklearn.log_model()` parameter should they use?

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C. `code_path=["path/to/preprocessing_utils.py"]`. The `code_path` parameter in MLflow's `log_model()` functions accepts a list of file paths for local Python code to be bundled with the model. These files are included in the model artifact and made available at serving time, allowing the served model to import the helper code.

A machine learning engineer needs to include a custom helper module `preprocessing_utils.py` when logging a scikit-learn model to MLflow for serving. Which `mlflow.sklearn.log_model()` parameter should they use?

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A machine learning engineer needs to include a custom helper module preprocessing_utils.py when logging a scikit-learn model to MLflow for serving. Which mlflow.sklearn.log_model() parameter should they use?

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Discussion

A machine learning engineer needs to include a custom helper module `preprocessing_utils.py` when logging a scikit-learn model to MLflow for serving. Which `mlflow.sklearn.log_model()` parameter should they use?