[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117297-en":3,"doc-seo-117297-105":29,"detail-sidebar-cat-0-en-105":94},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117297,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Towards Next-Gen Machine Learning Asset Management Tools","Machine learning (ML) systems enable ML-enabled software to replace traditional code artifacts with ML-related assets, creating complex development and production challenges. The extended asset types and non-deterministic behavior of ML limit the effectiveness of conventional software engineering (SE) tooling, especially for model experimentation. This PhD research studies ML experiment management tools (ExMTs), evaluates their landscape, and identifies their benefits, limitations, and user burdens. Using knowledge- and solution-seeking research, it develops steps toward integrated, unified ExMTs and validates their effectiveness.","Thesis for The Degree of Doctor of Philosophy  \nTowards Next-Gen Machine Learning Asset Management Tools  \nIdowu O. Samuel  \nDivision of Interaction Design and Software Engineering Department of Computer Science & Engineering University of Gothenburg Gothenburg, Sweden, 2023  \nTowards Next-Gen Machine Learning Asset Management Tools  \nIdowu O. Samuel  \nCopyright ©2023 Idowu O. Samuel except where otherwise stated.  \nAll rights reserved.  \nDepartment of Computer Science & Engineering Division of Interaction Design and Software Engineering University of Gothenburg  \nGothenburg, Sweden  \nThis thesis has been prepared using LATEX.  \nPrinted by Chalmers Reproservice, Gothenburg, Sweden 2023 .  \n“To know, is to know that you know nothing. That is the meaning of true knowledge.”  \n- Socrates  \niv  \nAbstract  \nContext: The proficiency of machine learning (ML) systems in solving many realworld problems effectively has enabled a paradigm shift toward ML-enabled systems. In ML-enabled software, significant software code artifacts (i.e., assets) are replaced by ML-related assets, introducing multiple system development and production challenges. In particular, the need to manage extended asset types introduced by ML systems and the non-deterministic nature of ML make using traditional software engineering (SE) tools ineffective. The lack of supporting tools makes it demanding to address the concerns of specific aspects of ML-enabled system development, such as model experimentation. Consequently, new tool classes are being introduced to address these challenges. ML experiment management tools (ExMT) are examples of such tools aiming to mitigate the challenges and users’ burden of managing ML-specific assets. Although these tools have recently become available, they are, unfortunately, not fully mature and have the potential for several improvements. For instance, many practitioners still consider ExMTs costly, restrictive, and ineffective. These challenges imply the need for improvements in many areas and raise research questions about the appropriate characteristics of a useful and effective ExMT for managing the development assets of ML-enabled systems.  \nObjective: This PhD research aims to contribute to the rapidly evolving space of new and improved ExMTs to facilitate the development of improved tools targeting combined SE and data science use cases. Consequently, we contributed to the knowledge and extended insights on ML experiment, their assets, the ExMT’s landscape, and their benefits and effectiveness. We later proposed steps towards integrated ExMTsand artifacts based on the obtained insights.  \nMethod: We addressed our objectives by adopting 1) knowledge-seeking research, including exploratory studies, literature reviews, feature surveys, practitioner surveys, and controlled experiments, and 2) solution-seeking research, including design science proposing unified concepts from multiple tools. The former was used to understand ML experiments, the challenges of managing experiment assets, the state of practice and landscape of existing ExMTs, and their effectiveness, benefits, and limitations. The acquired insights are then leveraged to propose research steps in the later part toward integrated ExMTs using design science to develop a blueprint for unified management tools.  \nResults: This thesis presents seven significant results. First, it provides an empirically informed overview of the challenges in ML experiment management. Second, it presents insights into the types of ML-based projects, their development activities, and evolution patterns. Third, it offers an overview of existing tools, shedding light on the state of practice and research on asset management tools for ML experiments. Fourth, it presents an empirical-based report on the benefits and challenges of ExMTs. Fifth, it establishes the effectiveness of ExMTs in improving user performance. Sixth, it proposes a step-by-step guide toward integrated ML tools for SE an","cbCaigcT3wKEMFcx","https://ap.wps.com/l/cbCaigcT3wKEMFcx","pdf",2275293,1,80,"English","en",105,"# Abstract\n## Context\n## Objective\n## Method\n## Results\n## Conclusion\n# Acknowledgments","[{\"question\":\"Why are traditional software engineering tools insufficient for ML-enabled systems?\",\"answer\":\"ML-enabled software introduces extended asset types and non-deterministic ML behavior, making traditional SE tools ineffective for managing ML experiment-related development artifacts.\"},{\"question\":\"What is the main objective of this PhD research?\",\"answer\":\"To contribute to new and improved ML experiment management tools by facilitating integrated SE and data science use cases, expanding knowledge on ML experiments and their assets, and advancing integrated ExMT steps.\"},{\"question\":\"How does the thesis approach its research objectives?\",\"answer\":\"It uses knowledge-seeking methods (exploratory studies, literature reviews, feature and practitioner surveys, controlled experiments) and solution-seeking research (design science proposing unified concepts from multiple tools).\"},{\"question\":\"What key outputs does the thesis provide?\",\"answer\":\"It reports seven significant results: an empirical overview of ML experiment management challenges, project and evolution insights, an overview of existing tools, empirical findings on ExMT benefits and challenges, evidence of effectiveness on user performance, and a step-by-step guide plus a prototype/blueprint for unified 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are traditional software engineering tools insufficient for ML-enabled systems?","Question",{"text":74,"@type":75},"ML-enabled software introduces extended asset types and non-deterministic ML behavior, making traditional SE tools ineffective for managing ML experiment-related development artifacts.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What is the main objective of this PhD research?",{"text":79,"@type":75},"To contribute to new and improved ML experiment management tools by facilitating integrated SE and data science use cases, expanding knowledge on ML experiments and their assets, and advancing integrated ExMT steps.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the thesis approach its research objectives?",{"text":83,"@type":75},"It uses knowledge-seeking methods (exploratory studies, literature reviews, feature and practitioner surveys, controlled experiments) and solution-seeking research (design science proposing unified concepts from multiple tools).",{"name":85,"@type":72,"acceptedAnswer":86},"What key outputs does the thesis provide?",{"text":87,"@type":75},"It reports seven significant results: an empirical overview of ML experiment management challenges, project and evolution insights, an overview of existing tools, empirical findings on ExMT benefits and challenges, evidence of effectiveness on user performance, and a step-by-step guide plus a prototype/blueprint for unified ExMTs.","https://schema.org",{"og:url":51,"og:type":90,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":92,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":95},[96,100,103,107,112,117,122,125,130,133,137],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Story & 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