{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:12:51Z","timestamp":1780614771496,"version":"3.54.1"},"reference-count":14,"publisher":"Walter de Gruyter GmbH","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Finding and implementing a suitable machine learning (ML) solution to a task at hand has several facets. The technical side of ML has widely been discussed in detail, see, e.\u2009g., (Heizmann, M., A. Braun, M. H\u00fcttel, C. Kl\u00fcver, E. Marquardt, M. Overdick and M. Ulrich. 2020. Artificial Intelligence with Neural Networks in Optical Measurement and Inspection Systems. at \u2013 Automatisierungstechnik 68(6): 477\u2013487). This contribution focusses on the industrial implementation issues of ML projects, particularly for machine vision (MV) tasks. Especially in small and medium-sized enterprises (SMEs), resources cannot be activated at will in order to use a new technology like ML. We take this into account by, on the one hand, helping to realistically evaluate the opportunities and challenges involved in implementing ML projects for a given task. On the other hand, we consider not only technical aspects, but also organizational, social and customer-related ones. It is discussed which know-how a company itself has to bring into an ML project and which tasks can also be performed by service providers. Here, it becomes clear that ML techniques can be used at different levels of detail. The question of \u201cmake or buy\u201d is therefore also an entrepreneurial one when introducing ML into one\u2019s own products and processes, and must be answered with a view to one\u2019s own possibilities and structures.<\/jats:p>","DOI":"10.1515\/auto-2021-0149","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T13:16:53Z","timestamp":1641993413000},"page":"90-101","source":"Crossref","is-referenced-by-count":14,"title":["Implementing machine learning: chances and challenges"],"prefix":"10.1515","volume":"70","author":[{"given":"Michael","family":"Heizmann","sequence":"first","affiliation":[{"name":"Karlsruhe Institute of Technology (KIT) , Karlsruhe , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Braun","sequence":"additional","affiliation":[{"name":"University of Applied Sciences D\u00fcsseldorf , D\u00fcsseldorf , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Glitzner","sequence":"additional","affiliation":[{"name":"MVTec Software GmbH , M\u00fcnchen , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"G\u00fcnther","sequence":"additional","affiliation":[{"name":"Fraunhofer MEVIS , Bremen , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00fcnther","family":"Hasna","sequence":"additional","affiliation":[{"name":"ANSYS Germany GmbH , M\u00fcnchen , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Kl\u00fcver","sequence":"additional","affiliation":[{"name":"University of Duisburg-Essen , Essen , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob","family":"Kroo\u00df","sequence":"additional","affiliation":[{"name":"Helmut Schmidt University , Hamburg , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik","family":"Marquardt","sequence":"additional","affiliation":[{"name":"VDI e.\u2009V. , D\u00fcsseldorf , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Overdick","sequence":"additional","affiliation":[{"name":"SICK AG , Waldkirch , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Ulrich","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology (KIT) , Karlsruhe , Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2022,1,13]]},"reference":[{"key":"2023033111590338464_j_auto-2021-0149_ref_001","unstructured":"Deutsche Akademie der Technikwissenschaften (acatech). 2021. KI im Mittelstand \u2013 Potenziale erkennen, Voraussetzungen schaffen, Transformation meistern. https:\/\/www.acatech.de\/publikation\/ki-im-mittelstand-potenziale-erkennen-voraussetzungen-schaffen-transformation-meistern\/."},{"key":"2023033111590338464_j_auto-2021-0149_ref_002","unstructured":"European Commission. 2020. White Paper on Artificial Intelligence \u2013 A European approach to excellence and trust. https:\/\/ec.europa.eu\/info\/sites\/info\/files\/commission-white-paper-artificial-intelligence-feb2020_en.pdf."},{"key":"2023033111590338464_j_auto-2021-0149_ref_003","unstructured":"European Commission. 2021. A European approach to Artificial intelligence. https:\/\/digital-strategy.ec.europa.eu\/en\/policies\/european-approach-artificial-intelligence."},{"key":"2023033111590338464_j_auto-2021-0149_ref_004","unstructured":"European Commission. 2021. Proposal for a Regulation of the European Parliament and of the Council Laying down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts. https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?qid=1623335154975&uri=CELEX%3A52021PC0206."},{"key":"2023033111590338464_j_auto-2021-0149_ref_005","unstructured":"Evaluation of measurement data \u2013 Guide to the expression of uncertainty in measurement. 2018."},{"key":"2023033111590338464_j_auto-2021-0149_ref_006","doi-asserted-by":"crossref","unstructured":"Heizmann, M., A. Braun, M. H\u00fcttel, C. Kl\u00fcver, E. Marquardt, M. Overdick and M. Ulrich. 2020. Artificial Intelligence with Neural Networks in Optical Measurement and Inspection Systems. at \u2013 Automatisierungstechnik 68(6): 477\u2013487.","DOI":"10.1515\/auto-2020-0006"},{"key":"2023033111590338464_j_auto-2021-0149_ref_007","unstructured":"Kersten, W. and S. Lodemann. 2019. What impact does AI have on SME processes and employees? \u2013 An insight into supply chain management. https:\/\/www.hamburg-logistik.net\/fileadmin\/user_upload\/blog\/bilder\/Veranstaltungen\/VA_LIHH\/do.innovation\/2019\/E_-_TUHH_doinnovation.pdf."},{"key":"2023033111590338464_j_auto-2021-0149_ref_008","unstructured":"Koopman, P. 2018. The heavy tail safety ceiling. SAE Automated and Connected Vehicle Systems Testing Symposium, Greenville, USA, June 2018. https:\/\/users.ece.cmu.edu\/~koopman\/pubs\/koopman18_heavy_tail_ceiling.pdf."},{"key":"2023033111590338464_j_auto-2021-0149_ref_009","doi-asserted-by":"crossref","unstructured":"Krau\u00df, J., J. Dori\u00dfen, H. Mende, M. Frye and R.\u2009H. Schmitt. 2019. Machine learning and artificial intelligence in production: Application areas and publicly available data sets. In: (J.\u2009P. Wulfsberg, W. Hintze and B.-A. Behrens, eds) Production at the leading edge of technology. Springer, pp.\u2009493\u2013501.","DOI":"10.1007\/978-3-662-60417-5_49"},{"key":"2023033111590338464_j_auto-2021-0149_ref_010","doi-asserted-by":"crossref","unstructured":"Su, J., D.\u2009V. Vargas and K. Sakurai. 2019. One pixel attack for fooling deep neural networks. IEEE Transactions on Evolutionary Computation 23(5): 828\u2013841.","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"2023033111590338464_j_auto-2021-0149_ref_011","unstructured":"VDI Verein Deutscher Ingenieure e.\u2009V. 2019. Gesellschaft f\u00fcr Mess- und Automatisierungstechnik, Fachbereich Optische Technologien. Maschinelles Lernen \u2013 Neuronale Netze in optischen Mess- und Pr\u00fcfsystemen, VDI-Statusreport (in German). https:\/\/www.vdi.de\/ueber-uns\/presse\/publikationen\/details\/kuenstliche-intelligenz-mit-neuronalen-netzen-in-optischen-mess-und-pruefsystemen."},{"key":"2023033111590338464_j_auto-2021-0149_ref_012","unstructured":"VDI Verein Deutscher Ingenieure e.\u2009V. 2020. Gesellschaft f\u00fcr Mess- und Automatisierungstechnik, Fachbereich Optische Technologien. Maschinelles Lernen in KMU \u2013 K\u00fcnstliche Intelligenz im eigenen Unternehmen nutzen, VDI-Statusreport (in German). https:\/\/www.vdi.de\/ueber-uns\/presse\/publikationen\/details\/vdi-statusreport-maschinelles-lernen-in-kmu."},{"key":"2023033111590338464_j_auto-2021-0149_ref_013","unstructured":"VDI\/VDE\/VDMA 2017. 2632 Part 3: 2017-10 Machine vision\/industrial image processing \u2013 Acceptance test of classifying machine vision systems. Beuth Verlag, Berlin."},{"key":"2023033111590338464_j_auto-2021-0149_ref_014","unstructured":"VDI\/VDE\/VDMA. 2020. 2632 Part 3.1: 2020-08 Machine vision\/industrial image processing \u2013 Acceptance test of classifying machine vision systems \u2013 Test of classification performance. Beuth Verlag, Berlin."}],"container-title":["at - Automatisierungstechnik"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2021-0149\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2021-0149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T16:25:11Z","timestamp":1680279911000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/auto-2021-0149\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,1]]},"references-count":14,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1,13]]},"published-print":{"date-parts":[[2022,1,27]]}},"alternative-id":["10.1515\/auto-2021-0149"],"URL":"https:\/\/doi.org\/10.1515\/auto-2021-0149","relation":{},"ISSN":["2196-677X","0178-2312"],"issn-type":[{"value":"2196-677X","type":"electronic"},{"value":"0178-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2022,1,1]]}}}