In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences. We envision a capability-based framework, which uses fine-grained specifications for ML model behaviors to unite existing efforts towards better ML engineering. We use concrete scenarios (model design, debugging, and maintenance) to articulate capabilities' broad applications across various different dimensions, and their impact on building safer, more generalizable and more trustworthy models that reflect human needs. Through preliminary experiments, we show capabilities' potential for reflecting model generalizability, which can provide guidance for ML engineering process. We discuss challenges and opportunities for capabilities' integration into ML engineering.

翻译：尽管机器学习发展迅速，但其工程支撑分散于多种形式，且往往偏向特定工程阶段、利益相关方及评估偏好。我们构想了一种基于能力的框架，该框架通过对机器学习模型行为进行细粒度规范，以整合现有研究从而改进机器学习工程实践。通过模型设计、调试与维护等具体场景，我们阐述了能力框架在不同维度上的广泛应用，以及其对构建更安全、更泛化且更值得信赖、反映人类需求的模型所产生的影响。初步实验表明，能力框架能够反映模型的泛化能力，为机器学习工程流程提供指导。我们探讨了将能力框架融入机器学习工程所面临的挑战与机遇。