[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124923-en":3,"doc-seo-124923-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124923,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Examining Engineers’ Lived Experiences Deploying Machine Learning Production Models - A Phenomenological Study","This qualitative phenomenological study investigates machine learning (ML) model deployment challenges across the ML lifecycle in production environments through the lens of the technology acceptance model (TAM). The research addresses why many deployments fail by capturing ML engineers’ perceived usefulness and ease of use across five lifecycle stages: requirements analysis, data management, benchmarking metrics, user acceptance testing, and privacy policy. Data were collected via semi-structured interviews with 15 ML experts and analyzed using textural, structural, and textural-structural descriptions of lived experiences.","Examining Engineers’ Lived Experiences Deploying Machine Learning Production Models: A Phenomenological Study  \nDurga Devi Papineni  \nUniversity of the Cumberlands  \nMary L. Lind  \nLouisiana State University Shreveport  \nThis qualitative phenomenological study investigated machine learning (ML) model deployment challenges during the ML lifecycle using the theoretical framework of the technology acceptance model (TAM). Researchers have designed several frameworks for understanding the ML lifecycle, but those frameworks remain untested, and many ML model deployments still fail. The study’s central research question asked, what challenges do organizations face when deploying ML models in production environments? The phenomenological research design identified users’ perceptions and lived experiences deploying ML models in production environments. Data were collected via semi structured interviews with 15 ML experts. The phenomenon from the interviews was described in textural, structural, and textural-structural descriptions ofparticipants’ lived experiences.  \nKeywords: machine learning, deployment, phenomenological, technology acceptance  \nINTRODUCTION  \nThis qualitative phenomenological study investigated the challenges of machine learning (ML) model deployment in production environments by investigating the perceived usefulness and ease of use of five stages of the ML lifecycle. ML models play a significant role in wide-ranging business fields, including technology, health, security, and manufacturing (Chen et al., 2020) . Scholars have noted that many companies rely on ML models to increase process efficiency, develop innovative products, and improve service provision (Canhoto & Clear, 2020; Liu, 2020; Vincent-Lancrin & van der Vlies, 2020) . Unfortunately, organizations can face many challenges when deploying ML models in production environments (Baier et al., 2019; Cai et al., 2019; Garcia et al., 2020) .  \nBackground and Problem Statement  \nAs technology has become increasingly important today, ML has arisen as a fundamental method of extracting meaning from enormous quantities of data (Olowononi et al., 2021) . ML offers organizations many benefits, health outcome predictability (Engelhard et al., 2021), and organizational efficiency (Dankwa-Mullan et al., 2019; Wuest et al., 2016) . ML models also allow organizations and researchers to use novel analysis techniques that address poor data quality or complex datasets (Cai et al., 2019; Garcia et  \nal., 2020). Unfortunately, the many benefits of ML models cannot be obtained if the models are ineffectively deployed (Mueller & Massaron, 2021; Oakden-Rayner et al., 2020) .  \nBecause of the challenges associated with ML, most development projects underperform (Mueller & Massaron, 2021; Oakden-Rayner et al., 2020; Pohle, 2018). Approximately 80% of ML projects never reach the deployment stage, and of those that do, only 60% are productive (Pohle, 2018) . Based on these numbers, most ML projects never produce effective results, and the high failure rates suggest that investment in ML solutions can present risks for organizations (Mueller & Massaron, 2021) . ML deployment success and adoption rates are predicted to increase because of increased data availability and exceptionally adaptable models capable of adjusting to explicit business needs, but more research is needed to ensure ML models operate efficiently (Agrawal et al., 2020; Correia et al., 2021) .  \nThis study used a qualitative research design and a phenomenological approach to investigate ML model deployment challenges in production environments examining the perceived usefulness and ease of use of five stages of the ML lifecycle. The selected stages of the ML lifecycle were grounded in scholarly literature. These five stages included (a) requirements analysis, (b) data management, (c) benchmarking metrics,(d) user acceptance testing, and (e) privacy policy.  \nPurpose of the Study  \nThe purpose of this qualitative phe","cbCait6EQB2wBsYa","https://ap.wps.com/l/cbCait6EQB2wBsYa","pdf",441063,1,33,"English","en",105,"# Introduction\n## Background and Problem Statement\n## Purpose of the Study\n## Significance of the Study","[{\"question\":\"What central question does the study investigate about ML deployments?\",\"answer\":\"The study asks what challenges organizations face when deploying ML models in production environments.\"},{\"question\":\"How is the study framed theoretically?\",\"answer\":\"It uses the technology acceptance model (TAM) to examine perceived usefulness and ease of use during the ML lifecycle.\"},{\"question\":\"How were the study’s data collected and analyzed?\",\"answer\":\"Researchers conducted semi-structured interviews with 15 ML experts and described the phenomenon using textural, structural, and textural-structural accounts of participants’ lived experiences.\"}]","Examining Engineers’ Lived Experiences Deploying Machine Learning Production Models - A Phenomenological Study | PDF",1785895398,83,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"examining-engineers-lived-experiences-deploying-machine-learning-production-models-a-phenomenological-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/examining-engineers-lived-experiences-deploying-machine-learning-production-models-a-phenomenological-study/124923/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What central question does the study investigate about ML deployments?","Question",{"text":75,"@type":76},"The study asks what challenges organizations face when deploying ML models in production environments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the study framed theoretically?",{"text":80,"@type":76},"It uses the technology acceptance model (TAM) to examine perceived usefulness and ease of use during the ML lifecycle.",{"name":82,"@type":73,"acceptedAnswer":83},"How were the study’s data collected and analyzed?",{"text":84,"@type":76},"Researchers conducted semi-structured interviews with 15 ML experts and described the phenomenon using textural, structural, and textural-structural accounts of participants’ lived experiences.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]