In today's dynamic technological landscape, sustainability has emerged as a pivotal concern, especially with respect to architecting Machine Learning enabled Systems (MLS). Many ML models fail in transitioning to production, primarily hindered by uncertainties due to data variations, evolving requirements, and model instabilities. Machine Learning Operations (MLOps) offers a promising solution by enhancing adaptability and technical sustainability in MLS. However, MLOps itself faces challenges related to environmental impact, technical maintenance, and economic concerns. Over the years, self-adaptation has emerged as a potential solution to handle uncertainties. This paper introduces a novel approach employing self-adaptive principles integrated into the MLOps architecture through a MAPE-K loop to bolster MLOps sustainability. By autonomously responding to uncertainties, including data, model dynamics, and environmental variations, our approach aims to address the sustainability concerns of a given MLOps pipeline identified by an architect at design time. Further, we implement the method for a Smart City use case to display the capabilities of our approach.

翻译：在当今动态技术格局中，可持续性已成为关键关注点，尤其针对支持机器学习的系统（MLS）的架构设计。许多机器学习模型在向生产环境迁移时遭遇失败，主要受数据变化、需求演进和模型不稳定等不确定性因素的阻碍。机器学习运维（MLOps）通过增强MLS的适应性和技术可持续性提供了一种有前景的解决方案。然而，MLOps本身在环境影响、技术维护和经济考量方面仍面临挑战。近年来，自适应方法已成为应对不确定性的潜在方案。本文提出一种新颖方法，通过MAPE-K循环将自适应原则集成到MLOps架构中，以增强MLOps可持续性。通过自主响应数据、模型动态及环境变化等不确定性，我们的方法旨在解决架构师在设计阶段确定的特定MLOps管道的可持续性问题。此外，我们针对智慧城市用例实现了该方法，以展示该方法的实际能力。