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:

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  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.
  
  
  
  


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  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.