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
Herausgeber: Mora, Manuel; Duran-Limon, Hector A.; Wang, Fen; Gómez, Jorge Marx
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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: 1. 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...
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: 1. 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. 2. 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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