This research evaluates the extent to which modern AI frameworks, specifically TensorFlow and scikit-learn, adhere to the SOLID design principles - Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion. Analyzing the frameworks architectural documentation and design philosophies, this research investigates architectural trade-offs when balancing software engineering best practices with AI-specific needs. I examined each frameworks documentation, source code, and architectural components to evaluate their adherence to these principles. The results show that both frameworks adopt certain aspects of SOLID design principles but make intentional trade-offs to address performance, scalability, and the experimental nature of AI development. TensorFlow focuses on performance and scalability, sometimes sacrificing strict adherence to principles like Single Responsibility and Interface Segregation. While scikit-learns design philosophy aligns more closely with SOLID principles through consistent interfaces and composition principles, sticking closer to SOLID guidelines but with occasional deviations for performance optimizations and scalability. This research discovered that applying SOLID principles in AI frameworks depends on context, as performance, scalability, and flexibility often require deviations from traditional software engineering principles. This research contributes to understanding how domain-specific constraints influence architectural decisions in modern AI frameworks and how these frameworks strategically adapted design choices to effectively balance these contradicting requirements.

翻译：本研究评估了现代人工智能框架（特别是TensorFlow和scikit-learn）对SOLID设计原则（单一职责、开闭原则、里氏替换、接口隔离和依赖倒置）的遵循程度。通过分析框架的架构文档和设计理念，本研究探讨了在平衡软件工程最佳实践与人工智能特定需求时的架构权衡。作者通过审查各框架的文档、源代码和架构组件来评估其对上述原则的遵循情况。结果表明，两个框架均采纳了SOLID设计原则的某些方面，但为满足性能、可扩展性及人工智能开发的实验性特征而做出了有意识的权衡。TensorFlow侧重于性能和可扩展性，有时会牺牲对单一职责和接口隔离等原则的严格遵循。而scikit-learn的设计理念通过一致的接口和组合原则更紧密地贴合SOLID原则，虽更贴近SOLID准则，但偶尔会因性能优化和可扩展性需求而有所偏离。本研究发现，在人工智能框架中应用SOLID原则需视具体情境而定，因为性能、可扩展性和灵活性往往要求偏离传统的软件工程原则。本研究有助于理解特定领域约束如何影响现代人工智能框架的架构决策，以及这些框架如何通过策略性调整设计选择来有效平衡这些相互矛盾的需求。