Engineering and Management of Data Science, Analytics, and AI/ML Projects
Foundations, Models, Frameworks, Architectures, Standards, Processes, Practices, Platforms and Tools for Small and Big Data
Herausgegeben: Mora, Manuel; Marx Gómez, Jorge; Wang, Fen; Duran-Limon, Hector A.
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This book presents a dual perspective on modern research and praxis on Data Science, Analytics, and AI/Machine Learning (DSA-AI/ML) system with small or big data. Consequently, potential readers academics, researchers and practitioners interested in the systematic development and implementation of DSA-AI/ML systems can be benefited with the high-quality conceptual and empirical research chapters focused on:Foundations, Development Platforms, and Tools on Engineering and Management of DSA-AI/ML Projects:DSA-AI/ML reference architectures.Data visualization principles for DSA-AI/ML.Federated Lear...
This book presents a dual perspective on modern research and praxis on Data Science, Analytics, and AI/Machine Learning (DSA-AI/ML) system with small or big data. Consequently, potential readers academics, researchers and practitioners interested in the systematic development and implementation of DSA-AI/ML systems can be benefited with the high-quality conceptual and empirical research chapters focused on:
Foundations, Development Platforms, and Tools on Engineering and Management of DSA-AI/ML Projects:DSA-AI/ML reference architectures.Data visualization principles for DSA-AI/ML.Federated Learning in large-scale DSA-AI/ML systems.Achievements, Challenges, Trends, and Future Research Directions on DSA-AI/ML Projects:Large multimodal model-based simulation game for DSA-AI/ML systems.Value stream analysis and design applied to DSA-AI/ML systems.Quality management 4.0 and AI for DSA-AI/ML systems.
Hence, this research-oriented co-edited book contributes to achieve the systematic development and implementation of Data Science, Analytics, and AI/ML systems.
Foundations, Development Platforms, and Tools on Engineering and Management of DSA-AI/ML Projects:DSA-AI/ML reference architectures.Data visualization principles for DSA-AI/ML.Federated Learning in large-scale DSA-AI/ML systems.Achievements, Challenges, Trends, and Future Research Directions on DSA-AI/ML Projects:Large multimodal model-based simulation game for DSA-AI/ML systems.Value stream analysis and design applied to DSA-AI/ML systems.Quality management 4.0 and AI for DSA-AI/ML systems.
Hence, this research-oriented co-edited book contributes to achieve the systematic development and implementation of Data Science, Analytics, and AI/ML systems.
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