MLFlow is used to store model artifacts after each execution, and this process becomes part of the experiment.
Upon execution, a folder named "ml runs" is created in the current working directory, storing details of the executed models.
The MLFlow UI can be accessed through the command prompt, offering a simple interface to view experiments, models, metrics, and parameters.
Every execution has a unique ID, and within it, various metrics, parameters, and artifacts related to the model are logged.
MLFlow allows for comparison between different model runs, making it easier to observe changes in metrics across different executions, especially when data is randomly selected during train-test splits.

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