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

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.