Implementing machine learning: chances and challenges

Author:

Heizmann Michael1,Braun Alexander2,Glitzner Markus3,Günther Matthias4,Hasna Günther5,Klüver Christina6,Krooß Jakob7,Marquardt Erik8,Overdick Michael9,Ulrich Markus1

Affiliation:

1. Karlsruhe Institute of Technology (KIT) , Karlsruhe , Germany

2. University of Applied Sciences Düsseldorf , Düsseldorf , Germany

3. MVTec Software GmbH , München , Germany

4. Fraunhofer MEVIS , Bremen , Germany

5. ANSYS Germany GmbH , München , Germany

6. University of Duisburg-Essen , Essen , Germany

7. Helmut Schmidt University , Hamburg , Germany

8. VDI e. V. , Düsseldorf , Germany

9. SICK AG , Waldkirch , Germany

Abstract

Abstract 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. g., (Heizmann, M., A. Braun, M. Hüttel, C. Klüver, E. Marquardt, M. Overdick and M. Ulrich. 2020. Artificial Intelligence with Neural Networks in Optical Measurement and Inspection Systems. at – Automatisierungstechnik 68(6): 477–487). 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 “make or buy” is therefore also an entrepreneurial one when introducing ML into one’s own products and processes, and must be answered with a view to one’s own possibilities and structures.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Reference14 articles.

1. Deutsche Akademie der Technikwissenschaften (acatech). 2021. KI im Mittelstand – Potenziale erkennen, Voraussetzungen schaffen, Transformation meistern. https://www.acatech.de/publikation/ki-im-mittelstand-potenziale-erkennen-voraussetzungen-schaffen-transformation-meistern/.

2. European Commission. 2020. White Paper on Artificial Intelligence – A European approach to excellence and trust. https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf.

3. European Commission. 2021. A European approach to Artificial intelligence. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence.

4. 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.

5. Evaluation of measurement data – Guide to the expression of uncertainty in measurement. 2018.

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Uncertainty Quantification with Deep Ensembles for 6D Object Pose Estimation;ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences;2024-06-10

2. Uncertainty-aware Cross-Entropy for Semantic Segmentation;ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences;2024-06-10

3. FedUB: Federated Learning Algorithm Based on Update Bias;Mathematics;2024-05-20

4. Machine learning implementation in small and medium-sized enterprises: insights and recommendations from a quantitative study;Production Engineering;2024-04-10

5. Artificial Intelligence in Remanufacturing Contexts: Current Status and Future Opportunities;Lecture Notes in Mechanical Engineering;2024

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3