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.

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.

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.