[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117769-en":3,"doc-seo-117769-105":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},117769,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Comparison of Two Open Source Feature Stores for Explainable Machine Learning - Master’s thesis","Machine learning operations (MLOps) tools and practices enable continuous development and deployment of machine learning models as part of larger software systems. Explainable machine learning supports MLOps, and MLOps can benefit explanation efforts. Model outputs depend on the data and features used, so feature understanding is essential for explaining model decisions. The thesis examines how feature stores can help clarify features employed by learning models, comparing two open source systems—Feast and Hopsworks—from an explainability perspective.","Master’s thesis  \nMaster’s Programme in Computer Science  \nComparison of Two Open Source Feature Stores for Explainable Machine Learning  \nTintti Rahikainen  \nFebruary 12, 2023  \nFaculty of Science  \nUniversity of Helsinki  \nContact information  \nP. O. Box 68 (Pietari Kalmin katu 5)  \n00014 University of Helsinki,Finland  \n[Email address: info@cs.helsinki.fi](Email address: info@cs.helsinki.fi)  \nURL: [http://www.cs.helsinki.fi/](http://www.cs.helsinki.fi/)  \nHELSINGIN YLIOPISTO – HELSINGFORS UNIVERSITET – UNIVERSITY OF HELSINKI  \n\n| Tiedekunta—Fakultet—Faculty\u003Cbr>Faculty of Science |  | Koulutusohjelma—Utbildningsprogram—Study programme\u003Cbr>Master’s Programme in Computer Science |  |\n| --- | --- | --- | --- |\n| Tekijä—Författare—Author\u003Cbr>Tintti Rahikainen |  |  |  |\n| Työn nimi—Arbetets titel—Title\u003Cbr>Comparison of Two Open Source Feature Stores for Explainable Machine Learning |  |  |  |\n| Ohjaajat—Handledare—Supervisors\u003Cbr>D.Sc. Mikko Raatikainen, D.Sc. Saku Suuriniemi |  |  |  |\n| Työn laji—Arbetets art—Level\u003Cbr>Master’s thesis | Aika—Datum—Month and year\u003Cbr>February 12, 2023 |  | Sivumäärä—Sidoantal—Number of pages\u003Cbr>34 pages |\n| Tiivistelmä—Referat—Abstract\u003Cbr>Machine learning operations (MLOps) tools and practices help us continuously develop and deploy machine learning models as part of larger software systems. Explainable machine learning can support MLOps, and vice versa. The results of machine learning models are dependent on the data and features the models use, so understanding the features is important when we want to explain the decisions of the model.\u003Cbr>In this thesis, we aim to understand how feature stores can be used to help understand the features used by machine learning models. We compared two existing open source feature stores, Feast and Hopsworks, from an explainability point of view to explore how they can be used for explainable machine learning.\u003Cbr>We were able to use both Feast and Hopsworks to aid us in understanding the features we extracted from two different datasets. The feature stores have significant differences, Hopsworks being a part of a larger MLOps platform, and having more extensive functionalities.\u003Cbr>Feature stores provide useful tools for discovering and understanding the features for machine learning models. Hopsworks can help us understand the whole lineage of the data – where it comes from and how it has been transformed – while Feast focuses on serving the features consistently to models and needs complementing services to be as useful from an explainability point of view.\u003Cbr>ACM Computing Classification System (CCS)\u003Cbr>Software and its engineering → Software notations and tools → Software maintenance tools Software and its engineering → Software notations and tools → Software libraries and repositories |  |  |  |\n| Avainsanat—Nyckelord—Keywords\u003Cbr>MLOps, feature stores |  |  |  |\n| Säilytyspaikka—Förvaringsställe—Where deposited\u003Cbr>Helsinki University Library |  |  |  |\n| Muita tietoja—övriga uppgifter—Additional information\u003Cbr>Software study track |  |  |  |\n\nContents  \n1 Introduction 1  \n2 Background 4  \n2.1 Explainable AI (XAI) .............................. 4  \n2.2 Feature store . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7  \n3 Research method 11  \n4 Overview of the selected feature stores 14  \n4. 1 Feast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14  \n4.2 Hopsworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16  \n4.3 Summary .................................... 17  \n5 Results 19  \n5. 1 Feast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19  \n5.2 Hopsworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21  \n5.3 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25  \n6 Discussion 29  \n7 Conclusions 31  \nBibliography 32  \n1 Introduction  \nMachine learning systems are systems that include machine learnin","cbCaivOsFzvjeuuC","https://ap.wps.com/l/cbCaivOsFzvjeuuC","pdf",2443664,1,40,"English","en",105,"# Introduction\n# Background\n## Explainable AI (XAI)\n## Feature store\n# Research method\n# Overview of the selected feature stores\n## Feast\n## Hopsworks\n## Summary\n# Results\n## Feast\n## Hopsworks\n## Comparison\n# Discussion\n# Conclusions\nBibliography","[{\"question\":\"Why are feature stores important for explainable machine learning in MLOps?\",\"answer\":\"Feature store results help clarify which features a model uses, and model decisions depend on those data and features. Understanding features supports explanation while aligning with continuous development in MLOps.\"},{\"question\":\"Which two open source feature stores are compared in the thesis?\",\"answer\":\"The thesis compares Feast and Hopsworks. It evaluates them from an explainability viewpoint and uses them to analyze features extracted from two datasets.\"},{\"question\":\"What are the main differences between Feast and Hopsworks according to the results?\",\"answer\":\"Hopsworks is part of a broader MLOps platform and offers more extensive functionality, including tracing data lineage. Feast focuses on serving features consistently to models, requiring complementary services for explainability needs.\"}]","Comparison of Two Open Source Feature Stores for Explainable Machine Learning - Master’s thesis | PDF",1785679464,101,{"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},"comparison-of-two-open-source-feature-stores-for-explainable-machine-learning-masters-thesis","",{"@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/comparison-of-two-open-source-feature-stores-for-explainable-machine-learning-masters-thesis/117769/",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","2026-08-02",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},"Why are feature stores important for explainable machine learning in MLOps?","Question",{"text":75,"@type":76},"Feature store results help clarify which features a model uses, and model decisions depend on those data and features. Understanding features supports explanation while aligning with continuous development in MLOps.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which two open source feature stores are compared in the thesis?",{"text":80,"@type":76},"The thesis compares Feast and Hopsworks. It evaluates them from an explainability viewpoint and uses them to analyze features extracted from two datasets.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main differences between Feast and Hopsworks according to the results?",{"text":84,"@type":76},"Hopsworks is part of a broader MLOps platform and offers more extensive functionality, including tracing data lineage. 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