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A machine learning workflow describes the processes involved in machine learning work. Various stages help to universalize the process of building and maintaining machine learning networks.
By understanding these stages, pros figure out how to set up, implement and maintain a ML system.
Many experts identify aspects of machine learning workflow as stages, for example, gathering data, preprocessing, researching, and then training and testing the model, as well as the process of post-evaluation.
These important steps in the process serve to ensure that the machine learning project is centered toward success. Because machine learning conventionally uses training and test sets to set up machine learning functionality, the machine learning workflow is important to help achieve these results. Data scientists may be expected to be conversant in these aspects of machine learning development.