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Answer: To initiate the tracking of a new MLflow run with a specified name.
The `mlflow.start_run(run_name='Run name')` function is used to initiate the tracking of a new MLflow run with a specified name. This function creates a new entry in the tracking database to record all the information (parameters, metrics, artifacts) associated with this specific run. The `run_name` argument allows you to assign a meaningful name to this run for better organization and identification later. Other options are incorrect because: - Creating a new experiment is not the primary function of this call; it creates a new run within an existing experiment. - It does not just modify the name of an existing active run; it creates a new run. - Logging metrics is done within the active run context using separate `mlflow.log_metric` calls. - While you can start a new run without specifying a name, providing a descriptive name is recommended for better organization.
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What is the primary function of the mlflow.start_run(run_name='Run name') function in MLflow?
A
To log metrics for an existing MLflow run.
B
To initiate the tracking of a new MLflow run with a specified name.
C
To create a new experiment in MLflow.
D
To set the name of the active run in the MLflow experiment.
E
To start a new MLflow run without specifying a run name.