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Answer: mlflow.tensorflow.log_model
The correct choice is **B. mlflow.tensorflow.log_model** because it is specifically designed to save TensorFlow models in a format that includes the model graph. This functionality ensures that all details about the model architecture are captured within the experiment run. Other options like **mlflow.log_artifact** are too generic and do not offer the specialized features needed for TensorFlow models, such as automatic loading and versioning. Similarly, **mlflow.log_model** is a general-purpose function that lacks the specific capabilities required for TensorFlow models, and **mlflow.tensorflow.save_model** saves the model locally without integrating it into the MLflow experiment tracking system. Therefore, **mlflow.tensorflow.log_model** stands out as the most suitable option for logging the TensorFlow model graph in MLflow.
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A data scientist is working on a machine learning model using TensorFlow and wishes to document the model's architecture alongside the experiment. Which MLflow functionality should they use to log the TensorFlow model graph?
A
mlflow.log_artifact
B
mlflow.tensorflow.log_model
C
mlflow.log_model
D
mlflow.tensorflow.save_model