{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:26:36Z","timestamp":1774679196683,"version":"3.50.1"},"reference-count":98,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3181730","type":"journal-article","created":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T20:37:04Z","timestamp":1654807024000},"page":"63606-63618","source":"Crossref","is-referenced-by-count":91,"title":["MLOps: A Taxonomy and a Methodology"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8598-0137","authenticated-orcid":false,"given":"Matteo","family":"Testi","sequence":"first","affiliation":[{"name":"Integrated Research Centre, Università Campus Bio-Medico di Roma, Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2359-5214","authenticated-orcid":false,"given":"Matteo","family":"Ballabio","sequence":"additional","affiliation":[{"name":"DeepLearningItalia, Bergamo, Italy"}]},{"given":"Emanuele","family":"Frontoni","sequence":"additional","affiliation":[{"name":"Department of Political Sciences, Communication and International Relations, VRAI Laboratory, Università degli Studi di Macerata, Macerata, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3864-5800","authenticated-orcid":false,"given":"Giulio","family":"Iannello","sequence":"additional","affiliation":[{"name":"Department of Engineering, Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy"}]},{"given":"Sara","family":"Moccia","sequence":"additional","affiliation":[{"name":"The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2621-072X","authenticated-orcid":false,"given":"Paolo","family":"Soda","sequence":"additional","affiliation":[{"name":"Department of Engineering, Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0883-2691","authenticated-orcid":false,"given":"Gennaro","family":"Vessio","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Università degli Studi di Bari Aldo Moro, Bari, Italy"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIS.2016.7550882"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105117"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3001973"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-67716-9_2"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21918"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SIEDS.2018.8374722"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2016.01.006"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2008.01.006"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3004450"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.11648\/j.ijefm.20210906.14"},{"key":"ref11","article-title":"The economic benefits of standardization","author":"Blind","year":"2011"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3129454"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/978-1-4842-6549-9_3","article-title":"What is MLOps?","volume-title":"Beginning MLOps With MLFlow","author":"Alla","year":"2021"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/WAIN52551.2021.00024"},{"key":"ref15","article-title":"Machine learning explainability for external stakeholders","author":"Bhatt","year":"2020","journal-title":"arXiv:2007.05408"},{"issue":"3","key":"ref16","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/MS.2016.68","article-title":"DevOps","volume":"33","author":"Ebert","year":"2016","journal-title":"IEEE Softw."},{"key":"ref17","first-page":"2320","article-title":"DevOps automation and agile methodology","volume-title":"Proc. Int. J. Creative Res. Thoughts (IJCRT)","author":"Mohammad"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICSTW52544.2021.00039"},{"key":"ref19","article-title":"The collective knowledge project: Making ML models more portable and reproducible with open APIs, reusable best practices and MLOps","author":"Fursin","year":"2020","journal-title":"arXiv:2006.07161"},{"key":"ref20","first-page":"19","article-title":"Demystifying mlops for continuous delivery of the product","volume":"18","author":"Gujjar","year":"2022","journal-title":"Asian J. Adv. Res."},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/SANER53432.2022.00155"},{"key":"ref22","article-title":"DevOps","author":"Vizard","year":"2021"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/info11070363"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/MILCOM.2014.54"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.joule.2018.05.009"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.2478\/fcds-2020-0015"},{"key":"ref27","article-title":"Eckerson","author":"EckersonGroup","year":"2022"},{"key":"ref28","article-title":"Machine learning in production: A literature","author":"Zhao","year":"2021"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ESEM.2019.8870157"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2020.103298"},{"key":"ref31","article-title":"Engineering framework for scalable machine learning operations","author":"Scotton","year":"2021"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2017.8258038"},{"key":"ref33","first-page":"2503","article-title":"Hidden technical debt in machine learning systems","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Sculley"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/WAIN52551.2021.00026"},{"key":"ref35","article-title":"Monitoring and explainability of models in production","author":"Klaise","year":"2020","journal-title":"arXiv:2007.06299"},{"key":"ref36","article-title":"Towards automating the AI operations lifecycle","author":"Arnold","year":"2020","journal-title":"arXiv:2003.12808"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/WAIN52551.2021.00019"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00726-1"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1080\/17460441.2021.1932812"},{"issue":"4","key":"ref40","first-page":"39","article-title":"Accelerating the machine learning lifecycle with MLflow","volume":"41","author":"Zaharia","year":"2018","journal-title":"IEEE Data Eng. Bull."},{"key":"ref41","article-title":"Kedro-org\/Kedro: A Python framework for creating reproducible, maintainable and modular data science code","author":"Org"},{"key":"ref42","article-title":"Machine learning for business: Using Amazon SageMaker and Jupyter","author":"Hudgeon","year":"2020"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA45697.2020.00045"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS47924.2020.00042"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2019.00018"},{"issue":"4","key":"ref46","first-page":"13","article-title":"The CRISP-DM model: The new blueprint for data mining","volume":"5","author":"Shearer","year":"2000","journal-title":"J. Data Warehousing"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2017.080570"},{"key":"ref48","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"Hendrycks","year":"2019","journal-title":"arXiv:1903.12261"},{"key":"ref49","article-title":"Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models","author":"Samek","year":"2017","journal-title":"arXiv:1708.08296"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3390\/s20143817"},{"key":"ref51","article-title":"Explainable artificial intelligence approaches: A survey","author":"Rabiul Islam","year":"2021","journal-title":"arXiv:2101.09429"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/2593812.2593813"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CCWC54503.2022.9720902"},{"key":"ref54","article-title":"Machine learning operations: A survey on MLOps tool support","author":"Hewage","year":"2022","journal-title":"arXiv:2202.10169"},{"key":"ref55","volume-title":"AI Usecases & Tools to Grow Your Business","year":"2022"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020192224"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/2556288.2557238"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3076246.3076248"},{"key":"ref59","volume-title":"Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists","author":"Zheng","year":"2018"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1039\/C5SM01981H"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098043"},{"key":"ref63","first-page":"1","article-title":"Challenges in the deployment and operation of machine learning in practice","volume-title":"Proc. ECIS","author":"Baier"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.08.357"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.3390\/s18092946"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/3453444"},{"key":"ref67","article-title":"The state of MLOps","author":"Maya","year":"2021"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1016\/j.fcij.2016.04.001"},{"key":"ref69","article-title":"MLOps: 5 steps to operationalize machine-learning models\u2014AI4","author":"Agrawal"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.14778\/3467861.3467872"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.3390\/app11198861"},{"key":"ref72","volume-title":"Introducing MLOps: How to Scale Machine Learning in the Enterprise","author":"Treveil","year":"2020"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11283-1_1"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/MS.2015.50"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-64148-1_12"},{"key":"ref76","volume-title":"Jenkins: The Definitive Guide: Continuous Integration for Masses","author":"Smart","year":"2011"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-0076-6"},{"issue":"239","key":"ref78","first-page":"2","article-title":"Docker: Lightweight Linux containers for consistent development and deployment","volume":"2014","author":"Merkel","year":"2014","journal-title":"Linux J."},{"key":"ref79","volume-title":"PM. Helm","year":"2018"},{"key":"ref80","volume-title":"Kubernetes Manual","year":"2017"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2016.67"},{"key":"ref82","article-title":"Resources: Puppet","author":"Webteam"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/MS.2015.27"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-016-6903-6"},{"key":"ref85","article-title":"Retraining model during deployment: Continuous training and continuous testing","author":"Komolafe","year":"2021"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2011.06.019"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.3390\/e23010018"},{"key":"ref91","article-title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models","author":"Anthony","year":"2020","journal-title":"arXiv:2007.03051"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.3390\/en13205289"},{"key":"ref93","first-page":"1","article-title":"A definition of \u2018carbon footprint","volume":"1","author":"Wiedmann","year":"2008","journal-title":"Ecol. Econ. Res. trends"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1355"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.3390\/philosophies7010004"},{"key":"ref96","article-title":"White paper on artificial intelligence\u2014European commission","author":"European Commission"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9194578"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/IEMCON.2018.8615039"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09792270.pdf?arnumber=9792270","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T03:16:41Z","timestamp":1706757401000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9792270\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":98,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3181730","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}