[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116898-en":3,"doc-seo-116898-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},116898,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",6,"Technology","MACHINE LEARNING OPERATIONS ARCHITECTURE IN HEALTHCARE BIG DATA ENVIRONMENT - Batch versus online inference","Developing and operating machine learning systems involves uncertainties that exceed traditional software engineering. In clinical healthcare contexts, managing and reducing these uncertainties is essential for quality and reliability. MLOps addresses this need through processes and tools for controlled, automated, and monitored development and deployment. This thesis studies MLOps in healthcare big data, designing an architecture for an NLP solution and empirically comparing two deployment and inference approaches.","Ville Siltala  \nMACHINE LEARNING OPERATIONS ARCHITECTURE IN HEALTHCARE BIG DATA ENVIRONMENT  \nBatch versus online inference  \nFaculty of Information Technology and Communication Sciences  \nM. Sc. Thesis April 2023  \nABSTRACT  \nVille Siltala: Machine learning operations architecture in healthcare big data environment – batch versus online inference  \nM.Sc. Thesis  \nTampere University  \nMaster’s Degree Programme in Computer Science April 2023  \nDeveloping and operating machine learning systems is associated with uncertainties incomparable to traditional software engineering. Managing and mitigating these uncertainties is critical especially when creating machine learning systems for clinical healthcare use. By incorporating processes and tools to develop and deploy machine learning systems in a controlled, automated, and monitored manner, machine learning operations aims to ensure quality and reliability in machine learning systems.  \nThis study provides an examination of machine learning operations in the context of healthcare and big data. First, a study project was conducted to design a machine learning operations architecture for building a machine learning based NLP solution to be integrated into an existing clinical healthcare software application. Two separate model deployment and inference architectures were designed. To test the applicability of these architectures in the context of big data, an empirical study was conducted. The results showed the batch inference architecture using Spark NLP had better performance compared to a Docker container based online inference architecture.  \nIn conclusion, the study project involving the design of a machine learning operations architecture, as well as the empirical comparison of batch inference and online inference, offer insights into the field of machine learning operations. The proposed model and the results of the comparison can be used to develop machine learning systems and make informed decisions on the selection of an inference architecture.  \nKey words and terms: machine learning, machine learning operations, MLOps, natural language processing, NLP, big data, healthcare  \nTIIVISTELMÄ  \nVille Siltala: Koneoppimisen tuotantoarkkitehtuuri terveydenhuollon massadatakontekstissa –  \nvertailussa eräajo-ja reaaliaikainferenssi Pro gradu-tutkielma  \nTampereen yliopisto Tietojenkäsittelytieteiden tutkinto-ohjelma Huhtikuu 2023  \nKoneoppimiseen pohjaavien tietojärjestelmien kehittäminen ja operointi sisältää epävarmuustekijöitä, jotka eivät ole verrattavissa perinteiseen ohjelmistotuotantoon. Näiden epävarmuuksien hallinta ja lieventäminen on tärkeää etenkin luotaessa koneoppimisjärjestelmiä terveydenhuollon kliiniseen käyttöön. Koneoppimisen tuotanto (engl. Machine Learning Operations , MLOps) on joukko prosesseja ja työkaluja, joilla pyritään varmistamaan koneoppimisjärjestelmien laatu ja luotettavuus lisäämällä niiden kehitykseen ja tuotantoon automaatiota ja valvontaa.  \nTämä tutkielma tarkastelee koneoppimisen tuotantoa terveydenhuollon massadatankontekstissa. Ensin kuvataan tutkimusprojekti, jossa kehitettiin koneoppimisen tuotantoarkkitehtuuri NLP-sovelluksen (engl. Natural Language Processing) toteuttamiseksi. NLP-sovellus suunniteltiin integroitavaksi terveydenhuollon kliinisessä työssä käytettävään ohjelmistosovellukseen. Projektissa kuvattiin kaksi erilaista inferenssiarkkitehtuuria, joiden soveltuvuutta projektinkontekstissa testattiin empiirisellä tutkimuksella. Tulokset osoittivat Spark NLP-kirjastoon pohjautuvan eräajoinferenssin olevan tehokkaampi verrattaessa Docker-kontteihin perustuvaan reaaliaikainferenssiin.  \nTutkimusprojektissa kehitetty arkkitehtuuri ja prosessimalli tarjoavat esimerkin koneoppimisen tuotantoarkkitehtuurista hyödynnettäväksi koneoppimisjärjestelmien kehityksessä ja tuotannossa. Inferenssin suorituskykyä vertailevan empiirisen tutkimuksen tuloksia voidaan hyödyntää valittaessa inferenssiarkkitehtuuria tietoon pohjautuen.  \nAvainsanat: kone","cbCaik0EcmNUkJms","https://ap.wps.com/l/cbCaik0EcmNUkJms","pdf",1880752,1,50,"English","en",105,"# Contents\n## Introduction\n## Background\n## MLOps case study","[{\"question\":\"What problem does MLOps solve in machine learning systems for healthcare?\",\"answer\":\"MLOps helps manage and mitigate uncertainties to improve the quality and reliability of clinical healthcare machine learning systems.\"},{\"question\":\"What was the case study architecture designed for?\",\"answer\":\"A machine learning operations architecture was designed to build a machine learning-based NLP solution integrated into an existing clinical healthcare software application.\"},{\"question\":\"How did the two inference architectures compare in performance?\",\"answer\":\"The batch inference architecture using Spark NLP performed better than a Docker container-based online inference architecture in the empirical study.\"}]","MACHINE LEARNING OPERATIONS ARCHITECTURE IN HEALTHCARE BIG DATA ENVIRONMENT - Batch versus online inference | PDF",1785672335,126,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-operations-architecture-in-healthcare-big-data-environment-batch-versus-online-inference","",{"@graph":36,"@context":86},[37,54,69],{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-operations-architecture-in-healthcare-big-data-environment-batch-versus-online-inference/116898/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does MLOps solve in machine learning systems for healthcare?","Question",{"text":76,"@type":77},"MLOps helps manage and mitigate uncertainties to improve the quality and reliability of clinical healthcare machine learning systems.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What was the case study architecture designed for?",{"text":81,"@type":77},"A machine learning operations architecture was designed to build a machine learning-based NLP solution integrated into an existing clinical healthcare software application.",{"name":83,"@type":74,"acceptedAnswer":84},"How did the two inference architectures compare in performance?",{"text":85,"@type":77},"The batch inference architecture using Spark NLP performed better than a Docker container-based online inference architecture in the empirical study.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,113,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":21,"slug":112},"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":107,"slug":138},19,"General","general"]