Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11926
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Main Title: Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology
Author(s): Studer, Stefan
Bui, Thanh Binh
Drescher, Christian
Hanuschkin, Alexander
Winkler, Ludwig
Peters, Steven
Müller, Klaus-Robert
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/13132
http://dx.doi.org/10.14279/depositonce-11926
License: https://creativecommons.org/licenses/by/4.0/
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 face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning application. Business and data understanding are executed simultaneously in the first phase, as both have considerable impact on the feasibility of the project. The next phases are comprised of data preparation, modeling, evaluation, and deployment. Special focus is applied to the last phase, as a model running in changing real-time environments requires close monitoring and maintenance to reduce the risk of performance degradation over time. With each task of the process, this work proposes quality assurance methodology that is suitable to address challenges in machine learning development that are identified in the 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 fails to address machine learning specific tasks. The presented work proposes an industry- and application-neutral process model tailored for machine learning applications with a focus on technical tasks for quality assurance.
Subject(s): machine learning applications
quality assurance methodology
process model
automotive industry and academia
best practices
guidelines
Issue Date: 22-Apr-2021
Date Available: 19-May-2021
Language Code: en
DDC Class: 004 Datenverarbeitung; Informatik
Sponsor/Funder: BMBF, 01IS14013A, Verbundprojekt: BBDC - Berliner Kompetenzzentrum für Big Data
BMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and Data
BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Data
BMBF, 01GQ1115, D-JPN Verbund: Adaptive Gehirn-Computer-Schnittstellen (BCI) in nichtstationären Umgebungen
BMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, Zentrum
DFG, 390685689, EXC 2046: MATH+: Berlin Mathematics Research Center
DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlin
Journal Title: Machine Learning and Knowledge Extraction
Publisher: MDPI
Volume: 3
Issue: 2
Publisher DOI: 10.3390/make3020020
Page Start: 392
Page End: 413
EISSN: 2504-4990
TU Affiliation(s): Fak. 4 Elektrotechnik und Informatik » Inst. Softwaretechnik und Theoretische Informatik » FG Maschinelles Lernen
Appears in Collections:Technische Universität Berlin » Publications

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