[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117319-en":3,"doc-seo-117319-105":29,"detail-sidebar-cat-0-en-105":90},{"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},117319,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Collaborative Automated Machine Learning (AutoML) Process Framework - Study on SMEs","Rapid technological advancement and digital disruption intensify the pressure on small and medium enterprises (SMEs) to adopt data-driven practices for competitiveness and growth. Unlike large corporations, SMEs often lack resources, infrastructure, and specialized expertise to implement advanced analytics and machine learning. This study addresses that gap by developing a Collaborative Automated Machine Learning (AutoML) Process Framework for SMEs using Design Science Research. The framework conceptualizes, designs, and validates an accessible AutoML tool that automates complex workflows while enabling collaboration among stakeholders, improving operational efficiency, decision-making, and digital transformation while supporting inclusive innovation and economic resilience.","Collaborative automated machine learning (AutoML) process framework  \nJohnas Camillius Chami 1*, Vitor Santos2  \n1,2 Nova IMS-Nova Universidade de Lisboa, Portugal; [20220723@novaims.unl.pt](20220723@novaims.unl.pt) (J.C.C.) [vsantos@novaims.unl.pt](vsantos@novaims.unl.pt) (V.S.).  \nAbstract: In the face of rapid technological advancements and digital disruption, Small and Medium Enterprises (SMEs) grapple with integrating data-driven practices essential for competitiveness and growth. Unlike large corporations, SMEs often lack the resources and technical expertise to implement sophisticated data analytics and machine learning solutions. This study addresses the identified gap by developing a Collaborative Automated Machine Learning (AutoML) Process Framework tailored to the unique needs of SMEs. Leveraging Design Science Research methodology, the research conceptualizes, designs, and validates an accessible AutoML tool that automates complex machine learning processes while fostering collaboration among stakeholders. The framework aims to democratize advanced analytics, enabling SMEs to harness domain knowledge and drive data-driven decision-making without extensive data science expertise. The findings demonstrate that the proposed collaborative AutoML framework significantly enhances SMEs' operational efficiency, decision-making capabilities, and competitive edge, thereby contributing to their digital transformation and broader economic growth. This research not only bridges the existing gap in AutoML applications for SMEs but also aligns with sustainable development goals by promoting inclusive innovation and economic resilience.   \nKeywords: Automated machine learning (AutoML), Collaborative framework, Data-driven transformation, Design science research, Digital transformation, Small and medium enterprises (SMEs) .  \n1. Introduction  \nIn today's rapidly evolving digital landscape, Small and Medium Enterprises (SMEs) are under immense pressure to adopt data-driven practices to sustain and enhance their competitiveness. While large enterprises swiftly integrate advanced data analytics and machine learning (ML) solutions, SMEs often face significant barriers, including limited financial resources, lack of technical expertise, and the complexities associated with implementing such technologies. Ramos and Oliveira (2020) highlight that these challenges impede SMEs' ability to remain competitive in an increasingly data-centric market. Without the necessary infrastructure and expertise, SMEs struggle to leverage large-scale data analytics, underscoring the need for innovative solutions that democratize access to advanced analytics.  \nDespite the recognized importance of Automated Machine Learning (AutoML) in facilitating datadriven transformation, there remains a substantial research gap concerning the development of collaborative AutoML tools specifically designed for SMEs. Existing studies, such as those by Ramos and Oliveira (2020), acknowledge the obstacles SMEs encounter in adopting data-driven strategies but seldom explore the intricacies of creating collaborative AutoML solutions tailored to their unique operational contexts. Furthermore, Chang, Zhang, and Hussain (2019) focus primarily on the automation aspects of AutoML, neglecting the collaborative elements essential for SME operations. This gap highlights the need for a comprehensive exploration of how collaborative AutoML can democratize data-driven practices within SMEs, providing a foundation for designing and evaluating tools that align with their specific challenges and requirements.  \nThis research aims to address the aforementioned gap by designing a Collaborative AutoML Framework that empowers SMEs to utilize advanced machine learning algorithms without necessitating extensive data science expertise. The primary objectives ofthis study are:  \n● Comprehensive Literature Review: Analyze existing AutoML tools to understand their functionalities, benefits, and limi","cbCaitxlozy20QFp","https://ap.wps.com/l/cbCaitxlozy20QFp","pdf",223534,1,11,"English","en",105,"# 1. Introduction\n## Research gap and motivation\n## Objectives and significance\n# Abstract\n# Keywords","[{\"question\":\"Why do SMEs have difficulty adopting machine learning and analytics?\",\"answer\":\"SMEs often face barriers such as limited financial resources, lack of technical expertise, and the complexity of implementing such technologies, which prevents them from leveraging large-scale data analytics effectively.\"},{\"question\":\"What does the proposed Collaborative AutoML Process Framework aim to achieve?\",\"answer\":\"It aims to automate complex machine learning processes in an accessible way while fostering collaboration among stakeholders, enabling SMEs to use advanced analytics without extensive data science expertise.\"},{\"question\":\"How is the framework developed and validated in the study?\",\"answer\":\"Using Design Science Research methodology, the study conceptualizes, designs, and validates an AutoML tool through empirical validation to ensure it meets SMEs’ 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do SMEs have difficulty adopting machine learning and analytics?","Question",{"text":74,"@type":75},"SMEs often face barriers such as limited financial resources, lack of technical expertise, and the complexity of implementing such technologies, which prevents them from leveraging large-scale data analytics effectively.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What does the proposed Collaborative AutoML Process Framework aim to achieve?",{"text":79,"@type":75},"It aims to automate complex machine learning processes in an accessible way while fostering collaboration among stakeholders, enabling SMEs to use advanced analytics without extensive data science expertise.",{"name":81,"@type":72,"acceptedAnswer":82},"How is the framework developed and validated in the study?",{"text":83,"@type":75},"Using Design Science Research methodology, the study conceptualizes, designs, and validates an AutoML tool through empirical validation to ensure it meets SMEs’ 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