Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

Stefan Studer
* ORCID logo ,
Thanh Bui
* ,
Christian Drescher
,
Alexander Hanuschkin
,
Ludwig Winkler
,
Steven Peters
,
Klaus-Robert Müller
Version 1 : Received: 2 March 2021 / Approved: 3 March 2021 / Online: 3 March 2021 (14:11:09 CET)

A peer-reviewed article of this Preprint also exists.

Studer, S.; Bui, T.B.; Drescher, C.; Hanuschkin, A.; Winkler, L.; Peters, S.; Müller, K.-R. Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology. Mach. Learn. Knowl. Extr. 2021, 3, 392-413. Studer, S.; Bui, T.B.; Drescher, C.; Hanuschkin, A.; Winkler, L.; Peters, S.; Müller, K.-R. Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology. Mach. Learn. Knowl. Extr. 2021, 3, 392-413.

Abstract

Machine learning is an established and frequently used technique in industry and academia but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners have a need for guidance throughout the life cycle of a machine learning application to meet business expectations. We therefore propose a process model for the development of machine learning applications, that covers six phases from defining the scope to maintaining the deployed machine learning application. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent. With each task of the process, we propose quality assurance methodology that is suitable to address challenges in machine learning development that we identify in form of risks. The methodology is drawn from practical experience and scientific literature and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks. Our work proposes an industry and application neutral process model tailored for machine learning applications with focus on technical tasks for quality assurance.

Keywords

Machine Learning Applications; Quality Assurance Methodology; Process Model; Automotive Industry and Academia; Best Practices; Guidelines

Subject

Computer Science and Mathematics, Algebra and Number Theory

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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