The designated function retrieves stored outputs generated during MLflow runs. For example, after training a machine learning model and logging it as an artifact within an MLflow run, this functionality allows one to obtain a local copy of that model file for deployment or further analysis. It essentially provides a mechanism to access and utilize results saved during a tracked experiment.
The capability to retrieve these saved objects is essential for reproducible research and streamlined deployment workflows. It ensures that specific model versions or data transformations used in an experiment are easily accessible, eliminating ambiguity and reducing the risk of deploying unintended or untested components. Historically, managing experiment outputs was a manual and error-prone process; this functionality provides a programmatic and reliable solution.
