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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
Introduction
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.
Target Audience
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:
Topics Covered
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
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.
Conclusion
Hence, this research-oriented co-edited book contributes to achieve the systematic development and implementation of Data Science, Analytics, and AI/ML systems.
