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

Margaret Rouse 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 explanations have appeared on TechTarget websites and she's been cited as an authority in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine and Discovery Magazine.Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages. If you have a suggestion for a new definition or how to improve a technical explanation, please email Margaret or contact her…