Answer-first summary for fast verification
Answer: Both B and D
The correct answer is **E. Both B and D**. Here's why: - **`mlflow.log_artifact()`** (Option B) is designed for logging files, models, or images, not specifically for parameters, metrics, or tags. - **`mlflow.log_metric()`** (Option D) logs numerical metrics like accuracy or loss, indicating model performance. - **`mlflow.start_run()`** (Option C) initiates a new MLflow run but doesn't log parameters, metrics, or tags directly. - **`mlflow.set_tag()`** (Option A) assigns tags as key-value pairs for run organization. Given the question's focus on storing parameters, metrics, and tags, the best choice combines `mlflow.log_metric()` for metrics and `mlflow.set_tag()` for tags. Note that parameters are usually logged with `mlflow.log_param()`, not listed here, making **E** the optimal answer among the provided options.
Author: LeetQuiz Editorial Team
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A data scientist is utilizing MLflow for experiment tracking. They aim to store parameters, metrics, and tags for a model training run. Which MLflow function should they employ for this purpose?
A
mlflow.set_tag()
B
mlflow.log_artifact()
C
mlflow.start_run()
D
mlflow.log_metric()
E
Both B and D
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