Engineering and Management of Data Science, Analytics, and AI/ML Projects

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 readersacademics, researchers and practitioners interested in the systematic development and implementation of DSA-AI/ML systemscan 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.

  Inhalt

1.A Review of Main Non-Proprietary Domain- Independent Data Science Analytics AI/ML Reference Architectures a dual ISO/IEC/IEEE 42010 and IT Service Design Approach.- 2.Data Visualization in the Era of Data Science: a review.- 3.Requirements for using Federated Learning in Manufacturing Supply Chains.- 4.Large Multimodal Model-Based Simulation Game as a Socio-Technical System for Value Stream Analysis and Design.- 5.A Data-driven Clustering Approach for Assessing Service Performance of Brand Chains' Branches in the Food Service Industry Data Analytics Systems.- 6.Integrating Quality Management 4.0 with AI and Machine Learning.