{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T19:42:43Z","timestamp":1783194163087,"version":"3.54.6"},"reference-count":207,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Eng. Manage."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tem.2023.3287759","type":"journal-article","created":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T13:17:45Z","timestamp":1688649465000},"page":"7425-7455","source":"Crossref","is-referenced-by-count":9,"title":["Practices for Managing Machine Learning Products: A Multivocal Literature Review"],"prefix":"10.1109","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7254-0378","authenticated-orcid":false,"given":"Isaque","family":"Alves","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of S&#x00E3;o Paulo, S&#x00E3;o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5762-5413","authenticated-orcid":false,"given":"Leonardo A. F.","family":"Leite","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of S&#x00E3;o Paulo, S&#x00E3;o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8923-2814","authenticated-orcid":false,"given":"Paulo","family":"Meirelles","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of S&#x00E3;o Paulo, S&#x00E3;o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3888-7340","authenticated-orcid":false,"given":"Fabio","family":"Kon","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of S&#x00E3;o Paulo, S&#x00E3;o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3102-5166","authenticated-orcid":false,"given":"Carla Silva Rocha","family":"Aguiar","sequence":"additional","affiliation":[{"name":"Faculty of Gama, University of Brasilia, Brasilia, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-SEIP.2019.00042"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2019.2937083"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/RAISE.2019.00014"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58666-9_2"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3205946.3205960"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/EMR.2018.2870669"},{"key":"ref7","article-title":"How to make your company machine learning ready","author":"Hodson","year":"2016"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.bushor.2019.10.005"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/INDICON45594.2018.8987154"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3173346"},{"key":"ref11","article-title":"On challenges in machine learning model management","author":"Schelter","year":"2015","journal-title":"IEEE Data Eng. Bull."},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2946162"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-020-09894-9"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2020.100183"},{"key":"ref15","article-title":"MLOps: Continuous delivery and automation pipelines in machine learning","year":"2020"},{"key":"ref16","first-page":"2503","article-title":"Hidden technical debt in machine learning systems","volume-title":"Proc. 28th Int. Conf. Neural Inf. Process. Syst.","author":"Sculley","year":"2015"},{"key":"ref17","first-page":"89","article-title":"Understanding FLOSS through community publications: Strategies for grey literature review","volume-title":"Proc. IEEE\/ACM 42nd Int. Conf. Softw. Eng., New Ideas Emerg. Results","author":"Wen","year":"2020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3382494.3410681"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/SANER53432.2022.00029"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2022.3161161"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3453478"},{"issue":"1","key":"ref22","first-page":"108","article-title":"Artificial intelligence for the real world","volume":"96","author":"Davenport","year":"2018","journal-title":"Harvard Bus. Rev."},{"key":"ref23","article-title":"The team data science process lifecycle","year":"2021"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TEM.2020.2976642"},{"key":"ref25","volume-title":"Introduction to Machine Learning","author":"Alpaydin","year":"2020"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2022.3160276"},{"key":"ref27","first-page":"29","article-title":"CRISP-DM: Towards a standard process model for data mining","volume-title":"Proc. 4th Int. Conf. Practical Appl. Knowl. Discov. Data Mining","author":"Wirth","year":"2000"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10633"},{"key":"ref29","volume-title":"The Product Book","author":"Anon","year":"2017"},{"key":"ref30","volume-title":"The Guide to the Product Management and Marketing Body of Knowledge","author":"Eppinger","year":"2013"},{"key":"ref31","first-page":"985","article-title":"The role of a software product manager in various business environments","volume-title":"Proc. Federated Conf. Comput. Sci. Inf. Syst.","author":"Springer","year":"2018"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.4271\/2018-01-1075"},{"key":"ref33","first-page":"351","article-title":"Model governance: Reducing the anarchy of production ML","volume-title":"Proc. Annu. Tech. Conf.","author":"Sridhar","year":"2018"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2007.07.027"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2021.3134800"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-65854-0_8"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/SEAA.2018.00018"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/MSEC.2021.3076443"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2018.09.006"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1002\/0470870168"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1111\/jebm.12266"},{"key":"ref42","article-title":"Empirical standards for software engineering research","author":"Ralph","year":"2021"},{"key":"ref43","first-page":"155","article-title":"Grounded theory as an emergent method","volume":"155","author":"Charmaz","year":"2008","journal-title":"Handbook Emergent Methods"},{"key":"ref44","first-page":"1422","article-title":"An evidence-based inquiry into the use of grey literature in software engineering","author":"Zhang","year":"2020","journal-title":"Proc. IEEE\/ACM 42nd Int. Conf. Softw. Eng."},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1111\/ijmr.12102"},{"key":"ref46","first-page":"189","article-title":"How to treat the use of grey literature in software engineering","volume-title":"Proc. Int. Conf. Softw. Syst. Process.","author":"Zhou","year":"2020"},{"key":"ref47","article-title":"Software 2.0 vs software 1.0","author":"Fanous","year":"2020"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/1134285.1134500"},{"key":"ref49","article-title":"How prepared is your business to make the most of IA","author":"Zamora","year":"2018","journal-title":"Harvard Bus. Rev."},{"key":"ref50","article-title":"How to manage machine learning products,","author":"Huang","year":"2019"},{"key":"ref51","article-title":"Basics of data science product management: The ML workflow","author":"Ampil","year":"2019"},{"key":"ref52","article-title":"Data: A key requirement for your machine learning (ML) product","author":"Mewald","year":"2018"},{"key":"ref53","article-title":"7 elements of ai product strategy","author":"Chandrasekhar","year":"2020"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.bushor.2019.11.003"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/REW.2019.00049"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ESEM.2019.8870157"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/s00766-020-00343-z"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2008.04.003"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.01.228"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.09.140"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2020.106448"},{"key":"ref62","article-title":"A product managers guide to machine learning: Balanced scorecard","author":"Patha","year":"2020"},{"key":"ref63","article-title":"Product definition in the age of AI","author":"Mukherjee","year":"2018"},{"key":"ref64","article-title":"Machine learning products are built by teams, not unicorns","author":"Pinhasi","year":"2019"},{"key":"ref65","volume-title":"Artificial Intelligence: The Insights You Need From Harvard Business Review","author":"Davenport","year":"2019"},{"key":"ref66","article-title":"Managing data science as products","author":"Sheng","year":"2020"},{"key":"ref67","article-title":"Assessing the feasibility of a machine learning model","author":"Rand","year":"2021"},{"key":"ref68","article-title":"How to design an AI marketing strategy","author":"Thomas","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884783"},{"key":"ref70","article-title":"User needs + defining success","author":"Team","year":"2019"},{"key":"ref71","article-title":"Data collection + evaluation","author":"Team","year":"2019"},{"key":"ref72","article-title":"Understand machine learning and its end-to-end process","author":"Pandian","year":"2021"},{"key":"ref73","article-title":"Answering the big three data science questions at Cisco","author":"Griffin","year":"2020"},{"key":"ref74","article-title":"The importance of domain knowledge","author":"Yin","year":"2021"},{"key":"ref75","article-title":"How to organize deep learning projectsExamples of best practices, Blog post at Neptune","author":"Barla","year":"2021"},{"key":"ref76","article-title":"Agile development of data science projects","year":"2020"},{"key":"ref77","article-title":"How to set your AI project up for success","author":"Stackpole","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref78","article-title":"Three questions every ML product manager must answer","author":"Huang","year":"2020"},{"key":"ref79","article-title":"My framework for helping startups build and deploy data science","year":"2020"},{"key":"ref80","article-title":"Machine learning rules in a nutshell","author":"Dezhic","year":"2018"},{"key":"ref81","article-title":"Machine learning for product managers,","author":"Barlaskar","year":"2018"},{"key":"ref82","article-title":"Lean startup and machine learning,","author":"Mukherjee","year":"2018"},{"key":"ref83","article-title":"The step-by-step pm guide to building machine learning based products","author":"Gavish","year":"2017"},{"key":"ref84","article-title":"Building machine learning based products","author":"Hurlock","year":"2016"},{"key":"ref85","article-title":"A product management framework for machine learning","author":"Pathak","year":"2018"},{"key":"ref86","article-title":"Creating ML and AI productsThe smart products pipeline, Blog post at Medium","author":"Fabri","year":"2020"},{"key":"ref87","article-title":"Rules of machine learning: Best practices for ML engineering","author":"Zinkevich","year":"2019"},{"key":"ref88","article-title":"How no-code platforms can bring AI to small and midsize businesses","author":"Reilly","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300830"},{"key":"ref90","article-title":"Six important steps to build a machine learning system","author":"Agarwal","year":"2019"},{"key":"ref91","article-title":"Lessons from building ML based products","author":"Schfegger","year":"2019"},{"key":"ref92","article-title":"How IA willand wontchange the way you manage","author":"Canals","year":"2016","journal-title":"Harvard Bus. Rev."},{"key":"ref93","article-title":"Ask a techspert: How do machine learning models explain themselves?","author":"kerman","year":"2021"},{"key":"ref94","article-title":"Mental models","year":"2019"},{"key":"ref95","article-title":"AI adoption skyrocketed over the last 18 months","author":"McKendrick","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref96","article-title":"Shipping disruptive ML\/AI products at scale (not papers). The engineers take on scaling ML development processes","year":"2020"},{"key":"ref97","article-title":"How to build accountability into your AI","author":"Stanford","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref98","article-title":"When machine learning goes off the rails a guide to managing the risks","author":"Babic","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref99","article-title":"How to tell if machine learning can solve your business problem","volume":"5","author":"Fedyk","year":"2016","journal-title":"Harvard Bus. Rev."},{"key":"ref100","article-title":"Whats the role of designers while designing AI and ML products?","author":"Rodrigues","year":"2020"},{"key":"ref101","article-title":"Bringing design thinking to AI products","author":"Gottipati","year":"2019"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1145\/3233740.3233750"},{"key":"ref103","article-title":"The AI product owners tool kit","author":"Lianoudakis","year":"2020"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-19034-7_14"},{"key":"ref105","article-title":"The role of design in machine learning","author":"Schoppe","year":"2020"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3054782"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.14778\/3007263.3007318"},{"key":"ref108","article-title":"Good data analysis","author":"Riley","year":"2019"},{"key":"ref109","volume-title":"Customer Data and Privacy","year":"2019"},{"key":"ref110","article-title":"The key concept of scrum in machine learning","author":"Gupta","year":"2021"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/ICITA.2005.116"},{"key":"ref112","article-title":"Is your data infrastructure ready for AI?","author":"Earley","year":"2020","journal-title":"Harvard Bus. Rev."},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2018.00059"},{"key":"ref114","article-title":"MLOps: What it is, why it matters, and how to implement it (from a data scientist perspective","author":"Canuma","year":"2021"},{"key":"ref115","article-title":"Behind the buzzwords: How we build ML products at booking.com","author":"Harush","year":"2019"},{"key":"ref116","article-title":"The surprising truth about what it takes to build a machine learning product","author":"Cogan","year":"2019"},{"key":"ref117","article-title":"3 common problems with your machine learning product and how to fix them","author":"Mewald","year":"2018"},{"key":"ref118","article-title":"Build an ML product4 mistakes to avoid, Blog post at Towards Data Science","author":"Ataee","year":"2020"},{"key":"ref119","article-title":"MLOps: Real world model deployments","author":"Karbhari","year":"2020"},{"key":"ref120","article-title":"Machine learning pipeline: Architecture of ML platform in production","year":"2020"},{"key":"ref121","article-title":"MLOps: The epoch of productionizing ML models","author":"Soni","year":"2020"},{"key":"ref122","article-title":"Ml infrastructure tools for production","author":"Dhinakaran","year":"2020"},{"key":"ref123","article-title":"Hey Airbnb, do you want more dollar bills?","author":"Sharma","year":"2019"},{"key":"ref124","article-title":"Your deep-learning-tools-for-enterprises startup will fail","author":"Mewald","year":"2019"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/SEAA.2019.00030"},{"key":"ref126","article-title":"A look at machine learning system design","author":"Pathak","year":"2021"},{"key":"ref127","article-title":"Explainability + trust","year":"2019"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3148237"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1145\/3359981"},{"key":"ref130","article-title":"How to win with machine learning","author":"Agrawal","year":"2020","journal-title":"Harvard Bus. Rev."},{"key":"ref131","article-title":"AI-at-scale hinges on gaining a social license","author":"Candelon","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/MS.2022.3195432"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1145\/3487043"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1007\/s41315-021-00180-5"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1007\/s42486-021-00057-3"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2020.2980761"},{"key":"ref137","article-title":"2020 machine learning roadmap","author":"Bourke","year":"2020"},{"key":"ref138","article-title":"Feedback + control","year":"2019"},{"key":"ref139","article-title":"Data science project management in 2021The new guide for ML teams","author":"Canuma","year":"2021"},{"key":"ref140","article-title":"Getting AI to scale","author":"Fountaine","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref141","article-title":"The 4 biggest problems I met while building machine learning products","author":"Mat","year":"2021"},{"key":"ref142","article-title":"Do you know how your teams get work done?","author":"Murty","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref143","article-title":"Bayesian AB testingpart Iconversions","author":"Sureshkumar","year":"2021"},{"key":"ref144","article-title":"Why is re-training ML models important?","author":"Corona","year":"2021"},{"key":"ref145","article-title":"Baselines","author":"Li","year":"2021"},{"key":"ref146","article-title":"The perfect formula for fintech products: CX = ML UX","author":"Oleksyuk","year":"2019"},{"key":"ref147","article-title":"Deploying artificial intelligence in new product development","author":"Meister","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref148","article-title":"Why AI that teaches itself to achieve a goal is the next big thing","author":"Hume","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref149","article-title":"Everyone in your organization needs to understand AI ethics","author":"Ammanath","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref150","article-title":"AI\/ML: Shiny object or panacea?","author":"Dodson","year":"2018"},{"key":"ref151","article-title":"AI regulation is coming","author":"Candelon","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref152","article-title":"How machine learning pushes us to define fairness","author":"Weinberger","year":"2019","journal-title":"Harvard Bus. Rev."},{"key":"ref153","article-title":"Thinking through the ethics of new tech... before theres a problem","author":"Ammanath","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref154","article-title":"The human factor in AI-based decision-making","author":"Meissner","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref155","article-title":"A practical guide to building ethical AI","author":"Blackman","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref156","article-title":"AI should augment human intelligence, not replace it","author":"Meissner","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref157","article-title":"Exploring the impact of artificial intelligence: Prediction vs judgment","author":"Gans","year":"2019","journal-title":"Harvard Bus. Rev."},{"key":"ref158","article-title":"Errors + graceful failure","year":"2019"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2020.106368"},{"key":"ref160","article-title":"Data product & economy 2.0","author":"Xiao","year":"2021"},{"key":"ref161","article-title":"5 important things to keep in mind during data preprocessing! (specific to predictive models)","author":"Jangir","year":"2021"},{"key":"ref162","article-title":"Model risk management and the role of explainable models","author":"Malik","year":"2021"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.01.199"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3386137"},{"key":"ref165","article-title":"A quick guide to data science and machine learning","author":"Qureshi","year":"2021"},{"key":"ref166","article-title":"Data exploration","author":"Liu","year":"2021"},{"key":"ref167","article-title":"The real deal about synthetic data","author":"Lucini","year":"2021","journal-title":"Harvard Bus. Rev."},{"key":"ref168","article-title":"Machine learning for product managers","author":"Lathia","year":"2017"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1145\/3210548"},{"key":"ref170","article-title":"A product managers guide to MLOps++","author":"Ramani","year":"2021"},{"key":"ref171","article-title":"Data validation in machine learning is imperative, not optional","author":"Agarwal","year":"2021"},{"key":"ref172","article-title":"The overfitting iceberg","author":"Fang","year":"2021"},{"key":"ref173","article-title":"Building ML products: Common pitfalls that product managers should avoid","author":"Raghuwanshi","year":"2020"},{"key":"ref174","article-title":"AutoML: Making AI more accessible to businesses","author":"Alsisaria","year":"2021"},{"key":"ref175","article-title":"Data pre processing: A crucial element of analytics driven embedded systems","author":"Pilotte","year":"2016"},{"key":"ref176","article-title":"AI and the 4 keys to its success: Diving deeper","author":"McKinney","year":"2019"},{"key":"ref177","article-title":"Improving model management practices for machine learning teams","author":"Uddin","year":"2021"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3054775"},{"issue":"7","key":"ref179","article-title":"What every manager should know about machine learning","volume":"93","author":"Yeomans","year":"2015","journal-title":"Harvard Bus. Rev."},{"key":"ref180","article-title":"Feature engineering (feature improvements scaling)","author":"Pandian","year":"2021"},{"key":"ref181","article-title":"How to reduce annotation when evaluating AI systems","author":"Jagannathan","year":"2020"},{"key":"ref182","article-title":"The four maturity levels of ML production systems","author":"Flender","year":"2021"},{"key":"ref183","article-title":"How to reduce communication overhead for database queries by up to 97","author":"Parchas","year":"2020"},{"key":"ref184","article-title":"How to build data engineering pipelines at scale","author":"Gupta","year":"2021"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2020.110542"},{"key":"ref186","article-title":"Speeding training of decision trees","author":"Han","year":"2020"},{"key":"ref187","article-title":"A comprehensive guide on feature engineering","author":"Kalyan","year":"2022"},{"key":"ref188","article-title":"Getting value from machine learning isnt about fancier algorithmsIts about making it easier to use","author":"Schreck","year":"2018","journal-title":"Harvard Bus. Rev."},{"key":"ref189","article-title":"Dont let your models quality drift away","author":"Oleszak","year":"2021"},{"key":"ref190","article-title":"An end-to-end guide to model explainability","author":"Dalmia","year":"2022"},{"key":"ref191","article-title":"A guide to machine learning pipelines and orchest","author":"Awan","year":"2022"},{"key":"ref192","article-title":"Assessing prediction accuracy of machine learning models","author":"Toffel","year":"2020","journal-title":"Harvard Bus. Rev."},{"key":"ref193","article-title":"Interpretability","author":"Huang","year":"2021"},{"key":"ref194","article-title":"Effective AI infrastructure or why feature store is not enough","author":"Baku","year":"2021"},{"key":"ref195","article-title":"How to implement data engineering in practice?","author":"Vianna","year":"2022"},{"key":"ref196","article-title":"How to productize ML faster with MLOps automation","author":"Haviv","year":"2020"},{"key":"ref197","article-title":"MLOpsThe why and the what","year":"2021"},{"key":"ref198","article-title":"A review of 2021 and trends in 2022A technical overview of the data industry!","author":"Raheja"},{"key":"ref199","article-title":"6 tips for MLOps acceleration & simplification","author":"Haviv","year":"2021"},{"key":"ref200","article-title":"Is data science dead in 10 years?","author":"Jee","year":"2021"},{"key":"ref201","article-title":"A guide to deploying machine\/deep learning model(s) in production","author":"Kumar","year":"2018"},{"key":"ref202","article-title":"How to run a data science team: TDSP and crisp methodologies","author":"Rodrigues","year":"2019"},{"key":"ref203","article-title":"Yet another full stack data science project,","author":"Vuppuluri","year":"2019"},{"key":"ref204","article-title":"KDD: Where linear methods still have their place","author":"Hardesty","year":"2020"},{"key":"ref205","article-title":"How to train large graph neural networks efficiently","author":"Zheng","year":"2021"},{"key":"ref206","article-title":"The importance of data drift detection that data scientists do not know","author":"Saikia","year":"2022"},{"key":"ref207","article-title":"Machine learning modelServerless deployment, Blog post at Analytics Vidhya","author":"Ponnada","year":"2021"}],"container-title":["IEEE Transactions on Engineering Management"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/17\/10339242\/10175022.pdf?arnumber=10175022","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T04:49:38Z","timestamp":1725598178000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10175022\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":207,"URL":"https:\/\/doi.org\/10.1109\/tem.2023.3287759","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.21960170.v3","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.21960170.v2","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.21960170","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.21960170.v1","asserted-by":"object"}]},"ISSN":["0018-9391","1558-0040"],"issn-type":[{"value":"0018-9391","type":"print"},{"value":"1558-0040","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}