Wow! eBook
Engineering and Management of Data Science, Analytics, and AI/ML Projects: Foundations, Models, Frameworks, Architectures, Standards, Processes, …
Author(s): Manuel Mora (Editor), Jorge Marx Gómez (Editor), Fen Wang (Editor), Hector A. Duran-Limon (Editor)
- Publisher: Springer
- Publication Date: 16 Nov. 2025
- Language: English
- Print length: 154 pages
- ISBN-10: 3032068886
- ISBN-13: 9783032068880
Book Description
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.
Editorial Reviews
From the Back Cover
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.
