Engineering and Management of Data Science, Analytics, and Ai/ML Projects: Foundations, Models, Frameworks, Architectures, Standards, Processes, Pract
暫譯: 數據科學、分析及人工智慧/機器學習專案的工程與管理：基礎、模型、框架、架構、標準、流程、實踐

Mora, Manuel, Gómez, Jorge Marx, Wang, Fen

Engineering and Management of Data Science, Analytics, and Ai/ML Projects: Foundations, Models, Frameworks, Architectures, Standards, Processes, Pract
  • 出版商: Springer
  • 出版日期: 2025-11-16
  • 售價: $7,920
  • 貴賓價: 9.5 折 $7,524
  • 語言: 英文
  • 頁數: 139
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3032068886
  • ISBN-13: 9783032068880
  • 相關分類: Machine Learning、Data-visualization

    • 海外代購書籍(需單獨結帳)

商品描述

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.

商品描述(中文翻譯)

本書從雙重視角探討現代數據科學、分析及人工智慧/機器學習（Data Science, Analytics, and AI/Machine Learning，簡稱 DSA-AI/ML）系統在小型或大型數據上的研究與實踐。因此，潛在讀者——對 DSA-AI/ML 系統的系統開發與實施感興趣的學者、研究人員及實務工作者——可以從高品質的概念性與實證性研究章節中受益，這些章節專注於：

- DSA-AI/ML 專案的基礎、開發平台與工具：
- DSA-AI/ML 參考架構。
- DSA-AI/ML 的數據視覺化原則。
- 大規模 DSA-AI/ML 系統中的聯邦學習。

- DSA-AI/ML 專案的成就、挑戰、趨勢及未來研究方向：
- 基於大型多模態模型的 DSA-AI/ML 系統模擬遊戲。
- 應用於 DSA-AI/ML 系統的價值流分析與設計。
- DSA-AI/ML 系統的品質管理 4.0 與人工智慧。

因此，本書作為一本以研究為導向的共同編輯書籍，對於實現數據科學、分析及人工智慧/機器學習系統的系統開發與實施具有貢獻。

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