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Enabling Data-Driven Transformation for Small and Medium Enterprises through Collaborative AutoML (Design and Implementation)

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Resumo:This thesis addresses the critical need for accessible machine learning solutions within small and medium-sized enterprises (SMEs) by developing a collaborative AutoML framework. SMEs often lack the data science expertise and resources necessary to implement advanced machine learning algorithms, which hinders their ability to compete in a data-driven market. This research identifies the unique challenges faced by SMEs in adopting AI/ML technologies and proposes a practical framework to overcome these barriers. Utilizing a Design Science Research (DSR) approach, a thorough literature review was conducted, followed by the development of the AutoML framework, which was then validated through expert interviews. The framework focuses on aligning AI/ML initiatives with business objectives, managing data quality, customizing AutoML pipelines, and ensuring resource efficiency and data privacy. By democratizing access to machine learning, the proposed framework aligns with several United Nations Sustainable Development Goals: SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 10 (Reduced Inequalities), enabling SMEs across various industries to leverage AI for innovation and growth. The findings highlight the significant potential of AutoML to empower non-technical users within SMEs, facilitating data-driven decision-making and enhancing operational efficiency. The framework serves as a comprehensive guide for SMEs to harness the power of machine learning, offering practical steps from initial strategy to continuous improvement. This work contributes to the broader AI/ML literature by providing actionable insights and a validated framework tailored to the specific needs of SMEs, ultimately fostering a more inclusive and innovative technological landscape.
Autores principais:Chami, Johnas Camillius
Assunto:AutoML Small and Medium Enterprises Data-driven Collaborative Framework Sustainable Development SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 10 - Reduced inequalities SDG 17 - Partnerships for the goals
Ano:2024
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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