[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119191-en":3,"doc-seo-119191-105":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},119191,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Tools evolving AI systems via experiment management - A survey of machine learning practitioners","Artificial intelligence advances through machine learning, but translating model work into reliable development depends on managing artifacts and metadata across experiments. Experiment management tools (EMTs) are built to support practitioners, yet tool adoption, benefits, limitations, and challenges remain insufficiently understood. This thesis empirically investigates practitioners’ usage through an online questionnaire with 24 participants, collecting qualitative and quantitative evidence on how EMTs affect reproducibility, time savings, traceability, and result analysis, and on shortcomings such as missing features, quality issues, and integration barriers.","Tools evolving AI systems via experiment management  \nA survey of machine learning practitioners  \nBachelor of Science Thesis in Software Engineering and Management  \nCarl Vgfelt Nihlmar  \nDepartment of Computer Science and Engineering UNIVERSITY OF GOTHENBURG  \nCHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2023  \nThe Author grants to University of Gothenburg and Chalmers University of Technology the non-exclusive right to publish the Work electronically and in a noncommercial purpose make it accessible on the Internet.  \nThe Author warrants that he/she is the author to the Work, and warrants that the Work does not contain text, pictures or other material that violates copyright law.  \nThe Author shall, when transferring the rights of the Work to a third party (for example a publisher or a company), acknowledge the third party about this agreement. If the Author has signed a copyright agreement with a third party regarding the Work, the Author warrants hereby that he/she has obtained any necessary permission from this third party to let University of Gothenburg and Chalmers University of Technology store the Work electronically and make it accessible on the Internet.  \nPerception and usage of tools supporting experiment management efforts during machine learning development  \n-A survey of machine learning practitioners  \n© Carl V˚agfelt Nihlmar, January, 2023 .  \nSupervisor: Samuel Idowu  \nExaminer: Richard Berntsson Svensson  \nUniversity of Gothenburg  \nChalmers University of Technology  \nDepartment of Computer Science and Engineering SE-412 96 G¨oteborg  \nSweden  \nTelephone + 46 (0)31-772 1000  \nCover: Abstract visual of new inteligent system, generated via machine learning  \nDepartment of Computer Science and Engineering University of Gothenburg  \nChalmers University of Technology Gothenburg, Sweden 2023  \nTools evolving AI systems via experiment management: a survey of machine learning  \npractitioners  \nCarl Vgfelt Nihlmar  \nDepartment of Computer Science and Engineering  \nUniversity of Gothenburg  \nGothenburg, Sweden  \n[gusnihca@student.gu.se](gusnihca@student.gu.se)  \nAbstract—Artificial intelligence employs machine learning to create intelligent systems. Experiment management tools have been created to support machine learning practitioners in their development efforts relating to the management of artifacts and metadata. Although the technical capabilities of such tools in terms of features have been widely examined, which tools are used as well as the tool ´s benefits, limitations and challenges, remain unknown. This paper provides an empirical investigation addressing the questions previously stated. Those interested in gaining a better understanding of the users of these tools, such as tool developers and researchers looking for initial data on this topic, could find the results presented valuable. This was achieved by developing and distributing an online questionnaire to elicit qualitative and quantitative data concerning experiment management tools from 24 machine learning practitioners. Participants reported benefiting from the tools in areas such as reproducibility, time savings, traceability, and result analysis. Reported challenges and limitations of the tools included a lack of features, quality and integration with other systems. Many participants combined tools in order to achieve the desired workflow. The three most commonly used tools were TensorBoard, MLFlow, and SageMaker. The empirical contributions of the survey improved the understanding of experiment management tools from the perspective of machine learning practitioners. The data can be leveraged towards building better supporting tools for AI development and serve as a basis for further research in related areas.  \nIndex Terms—Experiment management tools, machine learning, experiment management, experiment tracking, artificial intelligence.  \nintelligence via different types of learning [1]  \nI. INTRODUCTION  \nMachine learning (ML) is cons","cbCaib6YGjzjpRvy","https://ap.wps.com/l/cbCaib6YGjzjpRvy","pdf",1815071,1,14,"English","en",105,"# Introduction\n# Background and Motivation\n## Challenges in Machine Learning Development\n## Complexity, Data, and Technical Debt\n# Experiment Management Tools\n## Purpose and Scope\n# Empirical Study Methodology\n## Online Questionnaire\n## Participant Profile\n# Results and Findings\n## Reported Benefits\n## Reported Challenges\n# Common Tools and Workflow Practices\n## TensorBoard, MLflow, SageMaker\n# Discussion and Implications\n# Conclusion","[{\"question\":\"What problem does the thesis address about experiment management tools in machine learning?\",\"answer\":\"It addresses the gap between widely examined technical capabilities of EMTs and the still-unknown aspects of which tools practitioners use and what benefits, limitations, and challenges they experience.\"},{\"question\":\"How was the empirical investigation conducted?\",\"answer\":\"The study developed and distributed an online questionnaire to 24 machine learning practitioners to elicit both qualitative and quantitative data about EMTs.\"},{\"question\":\"What benefits and challenges did participants report?\",\"answer\":\"Participants reported benefits for reproducibility, time savings, traceability, and result analysis, while challenges included lack of features, quality concerns, and limited integration with other systems.\"}]","Tools evolving AI systems via experiment management - 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