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
Language: English
Published by Springer, 2025
- Hardcover
- Used
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- Title
- 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
- Author
- Mora, Manuel (EDT); Gómez, Jorge Marx (EDT); Wang, Fen (EDT); Duran-limon, Hector A. (EDT)
- Publisher
- Springer
- Publication year
- 2025
- Condition
- As New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3032068886
- ISBN 13
- 9783032068880
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.
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
GreatBookPrices
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