人工智慧(AI)時代來臨，在軟硬體共同進步的情況下，人類擁有越來越多的能力去訓練更深更複雜的神經網絡，使得AI在處理大量數據、模式識別和決策制定方面更加精確和有效。這也衍生出了一項新興的AI服務「機器學習即服務(Machine Learning as a Service, MLaaS) 」，一個可以藉由資料預處理和自動建模協助企業進行數位轉型的軟體系統平台。然而，現有文獻對於MLaaS大多鑽研於技術方面的研究，針對此服務在商業環境中的策略發展所談甚少。因此本研究以I公司為例，探討如何建立起MLaaS平台的商業模式，以利此新興服務在台灣順利發展。
本研究採用個案研究法，透過深度訪談及觀察法蒐集初級資料，並透過整理訪談企業資料、產業報告、理論文獻等方式蒐集次級資料。經由分析I公司MLaaS產品之平台增長策略，本研究進一步提出文獻框架外的三項要素，利基市場優勢、價值共創之合作模式和廠商優化建議作為MLaaS發展策略的關鍵考量因素，並可通過推動平台飛輪之系統性思考、關鍵客戶活動設計以及多樣化的訂價機制，幫助MLaaS價值的創造、傳遞與獲取，進而建構出平台完整的商業模式和商業生態系統。

The era of Artificial Intelligence (AI) is coming, and with advancements in both software and hardware, humans have gained increasingly more capability to train deeper and more complex neural networks. This enables AI to excel in processing vast amounts of data, pattern recognition, and decision-making with greater precision and efficiency. This has also given rise to a new emerging AI service called "Machine Learning as a Service" (MLaaS), which is a software system platform that assists businesses in digital transformation through data preprocessing and automated modeling. However, existing literature predominantly focuses on technical research related to MLaaS, with limited discussion on its strategic development in a business environment. Therefore, this study takes Company I as an example to explore how to establish a business model for an MLaaS platform, aiming to facilitate the smooth development of this emerging service in Taiwan.
This study utilizes a case study approach, collecting primary data through in-depth interviews and observational methods. Secondary data is collected by organizing interview data, industry reports, and academic literature. By analyzing Company I's platform growth strategies for MLaaS products, this study further proposes three key factors beyond the existing literature framework: niche market advantage, collaborative models for value co-creation, and optimization recommendations for vendors. These factors serve as critical considerations for the development of MLaaS strategies. By promoting systematic thinking through the platform flywheel, designing key customer activities, and implementing diverse pricing mechanisms, MLaaS value creation, delivery, and acquisition can be facilitated, ultimately establishing a comprehensive business model and ecosystem for the platform.

摘要 i
Abstract ii
誌 謝 辭 iv
目 錄 v
第一章 緒 論 1
第一節 研究背景與動機 1
第二節 研究目的與研究問題 2
第二章 文獻探討 4
第一節 機器學習 4
第二節 MLaaS 7
第三節 企業AI發展趨勢 12
第四節 平台服務策略 15
第三章 研究方法 24
第一節 個案研究 24
第二節 資料蒐集與分析 26
第三節 研究流程 27
第四章 個案研究 29
第一節 個案背景 29
第二節 個案分析 34
第五章 結論與建議 49
第一節 研究結論 49
第二節 研究貢獻與建議 50
第三節 研究限制 51
參考文獻 52

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