Modern applications are increasingly driven by Machine Learning (ML) models whose non-deterministic behavior is affecting the entire application life cycle from design to operation. The pervasive adoption of ML is urgently calling for approaches that guarantee a stable non-functional behavior of ML-based applications over time and across model changes. To this aim, non-functional properties of ML models, such as privacy, confidentiality, fairness, and explainability, must be monitored, verified, and maintained. This need is even more pressing when modern applications operate in the edge-cloud continuum, increasing their complexity and dynamicity. Existing approaches mostly focus on i) implementing classifier selection solutions according to the functional behavior of ML models, ii) finding new algorithmic solutions to this need, such as continuous re-training. In this paper, we propose a multi-model approach built on dynamic classifier selection, where multiple ML models showing similar non-functional properties are made available to the application and one model is selected over time according to (dynamic and unpredictable) contextual changes. Our solution goes beyond the state of the art by providing an architectural and methodological approach that continuously guarantees a stable non-functional behavior of ML-based applications, is applicable to different ML models, and is driven by non-functional properties assessed on the models themselves. It consists of a two-step process working during application operation, where model assessment verifies non-functional properties of ML models trained and selected at development time, and model substitution guarantees a continuous and stable support of non-functional properties. We experimentally evaluate our solution in a real-world scenario focusing on non-functional property fairness.

翻译：现代应用日益由机器学习模型驱动，其非确定性行为正影响着从设计到部署的整个应用生命周期。机器学习的广泛普及迫切需要能够保证基于机器学习的应用随时间推移及模型变更过程中保持稳定非功能行为的方法。为此，必须对机器学习模型的隐私性、机密性、公平性和可解释性等非功能属性进行监控、验证与维护。当现代应用在边缘-云端连续体环境中运行时，这一需求尤为迫切，因为该环境增加了应用的复杂性与动态性。现有方法主要聚焦于：i) 根据机器学习模型的功能行为实现分类器选择方案，ii) 寻找满足该需求的算法解决方案（如持续重训练）。本文提出一种基于动态分类器选择的多模型方法，其中应用可利用多个具有相似非功能属性的机器学习模型，并根据动态且不可预测的上下文变化随时间选择某个模型。我们的解决方案超越了现有技术，提供了一种架构与方法的组合，能持续保证基于机器学习的应用具有稳定的非功能行为，适用于不同类型的机器学习模型，且由模型自身评估的非功能属性驱动。该方法包含在应用运行期间执行的两步流程：模型评估步骤验证开发阶段训练和选定的机器学习模型的非功能属性，模型替换步骤则确保非功能属性的持续稳定支持。我们在聚焦于非功能属性公平性的真实场景中对该解决方案进行了实验评估。