A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model.
Which of the following code blocks will accomplish this task inside of an existing MLflow run block?
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model.
Which of the following code blocks will accomplish this task inside of an existing MLflow run block?
A machine learning engineer who wants to log feature importance data from a CSV file within an MLflow run should use the function mlflow.log_artifact. This function is designed to log files or directories as artifacts of a run, allowing for easy tracking and analysis along with other relevant model information. Therefore, the correct code block to use is mlflow.log_artifact(importance_path, 'feature-importance.csv').
Doc: https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.log_artifact
D. mlflow.log_artifact(importance_path, "feature-importance.csv")
D. mlflow.log_artifact(importance_path, "feature-importance.csv")
D. mlflow.log_artifact(importance_path, "feature-importance.csv")
Agree!
Answer is D
mlflow does not have log_data method
Answer is D
By using the mlflow.log_artifact function, you can log the feature importance CSV file as an artifact within an existing MLflow run. Additionally, you can log the model using the appropriate MLflow flavor and optionally log the feature importance data as metrics for easier access and analysis. This approach ensures that all relevant information is logged and tracked within the same MLflow run.