The correct line of code to initiate the autolog process in MLflow is mlflow.autolog(). This command enables automatic logging for various metrics, parameters, and artifacts during your machine learning model training process, compatible with libraries like scikit-learn, TensorFlow, PyTorch, and XGBoost. It simplifies experiment tracking by reducing the need for manual logging. The other options, while useful for specific logging tasks, do not initiate autologging:

• mlflow.log_param() is for manually logging specific parameters.
• with mlflow.start_run(): defines a context for an MLflow run but does not trigger autologging.
• mlflow.log_metric() is used for manually logging metrics.