A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console.
Which of the following lines of code can they use to accomplish this task?
To enable MLflow Autologging for all machine learning libraries used in a notebook, regardless of the Databricks Runtime version or workspace configurations, the line of code to use is mlflow.autolog(). This function enables comprehensive autologging for each supported library that is imported, allowing the automatic logging of metrics, parameters, and models without the need for explicit log statements for each library.
The correct answer is E. mlflow.autolog()1. This function enables autologging for each supported library you have installed as soon as you import it1. It allows you to log metrics, parameters, and models without the need for explicit log statements
mlflow.autolog(): MLflow Autologging is enabled for all supported libraries without explicitly specifying each library. Doc: https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.autolog
Correct Answer: E https://mlflow.org/blog/2023/11/30/using-autolog/index.html#:~:text=MLflow's%20automatic%20logging%20functionality%20offers,specify%20what%20to%20capture%20manually.