{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T01:51:58Z","timestamp":1742953918014,"version":"3.40.3"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031457272"},{"type":"electronic","value":"9783031457289"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-45728-9_2","type":"book-chapter","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T09:05:25Z","timestamp":1697015125000},"page":"24-44","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Empowering Machine Learning Development with\u00a0Service-Oriented Computing Principles"],"prefix":"10.1007","author":[{"given":"Mostafa Hadadian Nejad","family":"Yousefi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viktoriya","family":"Degeler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Lazovik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Arpteg, A., Brinne, B., Crnkovic-Friis, L., Bosch, J.: Software engineering challenges of deep learning. In: 2018 44th euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 50\u201359. IEEE (2018)","DOI":"10.1109\/SEAA.2018.00018"},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020)","journal-title":"Inf. Fusion"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Barry, D.K., Dick, D.: Chapter 3 - web services and service-oriented architectures. In: Barry, D.K., Dick, D. (eds.) Web Services, Service-Oriented Architectures, and Cloud Computing (Second Edition), pp. 15\u201333. The Savvy Manager\u2019s Guides, Morgan Kaufmann, Boston (2013)","DOI":"10.1016\/B978-0-12-398357-2.00003-8"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Baylor, D., et al.: TFX: a TensorFlow-based production-scale machine learning platform. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1387\u20131395 (2017)","DOI":"10.1145\/3097983.3098021"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Bisong, E., Bisong, E.: Kubeflow and kubeflow pipelines. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, pp. 671\u2013685 (2019)","DOI":"10.1007\/978-1-4842-4470-8_46"},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Bodor, A., Hnida, M., Najima, D.: MLOps: overview of current state and future directions. In: Innovations in Smart Cities Applications Volume 6: The Proceedings of the 7th International Conference on Smart City Applications, pp. 156\u2013165. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-26852-6_14","DOI":"10.1007\/978-3-031-26852-6_14"},{"key":"2_CR7","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.promfg.2020.02.182","volume":"43","author":"C Briese","year":"2020","unstructured":"Briese, C., Schl\u00fcter, M., Lehr, J., Maurer, K., Kr\u00fcger, J.: Towards deep learning in industrial applications taking advantage of service-oriented architectures. Procedia Manuf. 43, 503\u2013510 (2020)","journal-title":"Procedia Manuf."},{"key":"2_CR8","unstructured":"Burns, B., Beda, J., Hightower, K., Evenson, L.: Kubernetes: up and running. O\u2019Reilly Media, Inc. (2022)"},{"issue":"5","key":"2_CR9","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/MIS.2022.3207860","volume":"37","author":"L Cao","year":"2022","unstructured":"Cao, L.: Beyond AutoML: mindful and actionable AI and AutoAI with mind and action. IEEE Intell. Syst. 37(5), 6\u201318 (2022)","journal-title":"IEEE Intell. Syst."},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Chaudhary, A., Choudhary, C., Gupta, M.K., Lal, C., Badal, T.: Microservices in Big Data Analytics: Second International, ICETCE 2019, Rajasthan, India, February 1st-2nd 2019. Revised Selected Papers, Springer Nature (2019). https:\/\/doi.org\/10.1007\/978-981-15-0128-9","DOI":"10.1007\/978-981-15-0128-9"},{"key":"2_CR11","doi-asserted-by":"publisher","first-page":"39285","DOI":"10.1109\/ACCESS.2020.2974188","volume":"8","author":"Z Ding","year":"2020","unstructured":"Ding, Z., Wang, S., Pan, M.: QoS-constrained service selection for networked microservices. IEEE Access 8, 39285\u201339299 (2020)","journal-title":"IEEE Access"},{"key":"2_CR12","unstructured":"Dobies, J., Wood, J.: Kubernetes operators: automating the container orchestration platform. O\u2019Reilly Media (2020)"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Dragoni, N., et al.: Microservices: yesterday, today, and tomorrow. Present Ulterior Softw. Eng., 195\u2013216 (2017)","DOI":"10.1007\/978-3-319-67425-4_12"},{"key":"2_CR14","unstructured":"Elshawi, R., Maher, M., Sakr, S.: Automated machine learning: state-of-the-art and open challenges. arXiv preprint arXiv:1906.02287 (2019)"},{"issue":"3","key":"2_CR15","doi-asserted-by":"publisher","first-page":"95","DOI":"10.4018\/JDM.2021070105","volume":"32","author":"M Fantinato","year":"2021","unstructured":"Fantinato, M., Peres, S.M., Kafeza, E., Chiu, D.K., Hung, P.C.: A review on the integration of deep learning and service-oriented architecture. J. Database Manage. (JDM) 32(3), 95\u2013119 (2021)","journal-title":"J. Database Manage. (JDM)"},{"key":"2_CR16","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1007\/s10796-016-9731-1","volume":"20","author":"M Garriga","year":"2018","unstructured":"Garriga, M., et al.: A structural-semantic web service selection approach to improve retrievability of web services. Inf. Syst. Front. 20, 1319\u20131344 (2018)","journal-title":"Inf. Syst. Front."},{"issue":"2","key":"2_CR17","first-page":"325","volume":"15","author":"P Gluzmann","year":"2015","unstructured":"Gluzmann, P., Panigo, D.: Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressions. Stand Genomic Sci. 15(2), 325\u2013349 (2015)","journal-title":"Stand Genomic Sci."},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Granlund, T., Kopponen, A., Stirbu, V., Myllyaho, L., Mikkonen, T.: MLOps challenges in multi-organization setup: Experiences from two real-world cases. In: 2021 IEEE\/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN), pp. 82\u201388 (2021)","DOI":"10.1109\/WAIN52551.2021.00019"},{"key":"2_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106622","volume":"212","author":"X He","year":"2021","unstructured":"He, X., Zhao, K., Chu, X.: AutoML: a survey of the state-of-the-art. Knowl.-Based Syst. 212, 106622 (2021)","journal-title":"Knowl.-Based Syst."},{"issue":"7","key":"2_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3543847","volume":"55","author":"S Idowu","year":"2022","unstructured":"Idowu, S., Str\u00fcber, D., Berger, T.: Asset management in machine learning: state-of-research and state-of-practice. ACM Comput. Surv. 55(7), 1\u201335 (2022)","journal-title":"ACM Comput. Surv."},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Isdahl, R., Gundersen, O.E.: Out-of-the-box reproducibility: a survey of machine learning platforms. In: 2019 15th International Conference on eScience (eScience), pp. 86\u201395. IEEE (2019)","DOI":"10.1109\/eScience.2019.00017"},{"issue":"12","key":"2_CR22","doi-asserted-by":"publisher","first-page":"445","DOI":"10.35940\/ijitee.L3322.1081219","volume":"8","author":"A Kavikondala","year":"2019","unstructured":"Kavikondala, A., Muppalla, V., Krishna Prakasha, K., Acharya, V.: Automated retraining of machine learning models. Int. J. Innov. Technol. Explor. Eng. 8(12), 445\u2013452 (2019)","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"2_CR23","unstructured":"Kim, G., Humble, J., Debois, P., Willis, J., Forsgren, N.: The DevOps handbook: how to create world-class agility, reliability, & security in technology organizations. IT Revolution (2021)"},{"key":"2_CR24","unstructured":"Klaise, J., Van Looveren, A., Cox, C., Vacanti, G., Coca, A.: Monitoring and explainability of models in production. arXiv preprint arXiv:2007.06299 (2020)"},{"key":"2_CR25","doi-asserted-by":"publisher","first-page":"31866","DOI":"10.1109\/ACCESS.2023.3262138","volume":"11","author":"D Kreuzberger","year":"2023","unstructured":"Kreuzberger, D., K\u00fchl, N., Hirschl, S.: Machine Learning Operations (MLOps): overview, definition, and architecture. IEEE Access 11, 31866\u201331879 (2023)","journal-title":"IEEE Access"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Leite, L., Rocha, C., Kon, F., Milojicic, D., Meirelles, P.: A survey of devops concepts and challenges. ACM Comput. Surv. 52(6) (2019)","DOI":"10.1145\/3359981"},{"key":"2_CR27","doi-asserted-by":"publisher","first-page":"88718","DOI":"10.1109\/ACCESS.2019.2926127","volume":"7","author":"D Li","year":"2019","unstructured":"Li, D., Ye, D., Gao, N., Wang, S.: Service selection with QoS correlations in distributed service-based systems. IEEE Access 7, 88718\u201388732 (2019)","journal-title":"IEEE Access"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Mboweni, T., Masombuka, T., Dongmo, C.: A systematic review of machine learning devops. In: 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), pp. 1\u20136. IEEE (2022)","DOI":"10.1109\/ICECET55527.2022.9872968"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"M\u00e4kinen, S., Skogstr\u00f6m, H., Laaksonen, E., Mikkonen, T.: Who needs MLOps: what data scientists seek to accomplish and how can MLOps help? In: 2021 IEEE\/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN), pp. 109\u2013112 (2021)","DOI":"10.1109\/WAIN52551.2021.00024"},{"key":"2_CR30","unstructured":"Newman, S.: Building Microservices. O\u2019Reilly Media, Inc. (2021)"},{"key":"2_CR31","unstructured":"Papazoglou, M.P.: Service-oriented computing: Concepts, characteristics and directions. In: Proceedings of the Fourth International Conference on Web Information Systems Engineering, 2003. WISE 2003, pp. 3\u201312. IEEE (2003)"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Polyzotis, N., Roy, S., Whang, S.E., Zinkevich, M.: Data management challenges in production machine learning. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. 1723\u20131726 (2017)","DOI":"10.1145\/3035918.3054782"},{"issue":"1","key":"2_CR33","doi-asserted-by":"publisher","first-page":"113","DOI":"10.5755\/j01.itc.49.1.23251","volume":"49","author":"IM Rabbani","year":"2020","unstructured":"Rabbani, I.M., Aslam, M., Enriquez, A.M.M., Qudeer, Z.: Service association factor (SAF) for cloud service selection and recommendation. Inf. Technol. Control 49(1), 113\u2013126 (2020)","journal-title":"Inf. Technol. Control"},{"key":"2_CR34","unstructured":"Raschka, S., Mirjalili, V.: Python machine learning: machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing Ltd (2019)"},{"issue":"6","key":"2_CR35","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1016\/S0016-3287(97)00026-8","volume":"29","author":"JR Ravetz","year":"1997","unstructured":"Ravetz, J.R.: The science of \u2018what-if?\u2019. Futures 29(6), 533\u2013539 (1997)","journal-title":"Futures"},{"key":"2_CR36","doi-asserted-by":"publisher","first-page":"5193","DOI":"10.1007\/s10664-020-09881-0","volume":"25","author":"V Riccio","year":"2020","unstructured":"Riccio, V., Jahangirova, G., Stocco, A., Humbatova, N., Weiss, M., Tonella, P.: Testing machine learning based systems: a systematic mapping. Empir. Softw. Eng. 25, 5193\u20135254 (2020)","journal-title":"Empir. Softw. Eng."},{"issue":"19","key":"2_CR37","doi-asserted-by":"publisher","first-page":"8861","DOI":"10.3390\/app11198861","volume":"11","author":"P Ruf","year":"2021","unstructured":"Ruf, P., Madan, M., Reich, C., Ould-Abdeslam, D.: Demystifying MLOps and presenting a recipe for the selection of open-source tools. Appl. Sci. 11(19), 8861 (2021)","journal-title":"Appl. Sci."},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Saltelli, A., et al.: Global sensitivity analysis: the primer. John Wiley & Sons (2008)","DOI":"10.1002\/9780470725184"},{"key":"2_CR39","unstructured":"Sculley, D., et al.: Hidden technical debt in machine learning systems. In: Advances in Neural Information Processing Systems 28 (2015)"},{"key":"2_CR40","doi-asserted-by":"crossref","unstructured":"Symeonidis, G., Nerantzis, E., Kazakis, A., Papakostas, G.A.: MLOps - definitions, tools and challenges. In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0453\u20130460 (2022)","DOI":"10.1109\/CCWC54503.2022.9720902"},{"key":"2_CR41","doi-asserted-by":"publisher","first-page":"63606","DOI":"10.1109\/ACCESS.2022.3181730","volume":"10","author":"M Testi","year":"2022","unstructured":"Testi, M., et al.: MLOps: a taxonomy and a methodology. IEEE Access 10, 63606\u201363618 (2022)","journal-title":"IEEE Access"},{"key":"2_CR42","first-page":"841","volume":"31","author":"S Wachter","year":"2017","unstructured":"Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv. JL Tech. 31, 841 (2017)","journal-title":"Harv. JL Tech."},{"issue":"4","key":"2_CR43","first-page":"39","volume":"41","author":"M Zaharia","year":"2018","unstructured":"Zaharia, M., et al.: Accelerating the machine learning lifecycle with MLflow. IEEE Data Eng. Bull. 41(4), 39\u201345 (2018)","journal-title":"IEEE Data Eng. Bull."},{"key":"2_CR44","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Yu, Y., Ding, B.: Towards MLOps: a case study of ml pipeline platform. In: 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE), pp. 494\u2013500 (2020)","DOI":"10.1109\/ICAICE51518.2020.00102"},{"key":"2_CR45","doi-asserted-by":"crossref","unstructured":"Zolkifli, N.N., Ngah, A., Deraman, A.: Version control system: a review. In: Procedia Computer Science, the 3rd International Conference on Computer Science and Computational Intelligence (ICCSCI 2018): Empowering Smart Technology in Digital Era for a Better Life, vol. 135, pp. 408\u2013415 (2018)","DOI":"10.1016\/j.procs.2018.08.191"}],"container-title":["Communications in Computer and Information Science","Service-Oriented Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45728-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T09:12:55Z","timestamp":1697015575000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45728-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031457272","9783031457289"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45728-9_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"12 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SummerSOC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Symposium and Summer School on Service-Oriented Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion, Crete","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"summersoc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.summersoc.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Full paper: 6 (16-21 pages),  Short paper: 2 (9-10 pages)","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}