Machine Learning Workflow

What Does Machine Learning Workflow Mean?

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.

Techopedia Explains Machine Learning Workflow

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.


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Margaret Rouse
Technology Expert

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.