Addressing runtime uncertainties in Machine Learning-Enabled Systems (MLS) is crucial for maintaining Quality of Service (QoS). The Machine Learning Model Balancer is a concept that addresses these uncertainties by facilitating dynamic ML model switching, showing promise in improving QoS in MLS. Leveraging this concept, this paper introduces SWITCH, an exemplar developed to enhance self-adaptive capabilities in such systems through dynamic model switching in runtime. SWITCH is designed as a comprehensive web service catering to a broad range of ML scenarios, with its implementation demonstrated through an object detection use case. SWITCH provides researchers with a flexible platform to apply and evaluate their ML model switching strategies, aiming to enhance QoS in MLS. SWITCH features advanced input handling, real-time data processing, and logging for adaptation metrics supplemented with an interactive real-time dashboard for enhancing system observability. This paper details SWITCH's architecture, self-adaptation strategies through ML model switching, and its empirical validation through a case study, illustrating its potential to improve QoS in MLS. By enabling a hands-on approach to explore adaptive behaviors in ML systems, SWITCH contributes a valuable tool to the SEAMS community for research into self-adaptive mechanisms for MLS and their practical applications.

翻译：应对机器学习启用系统（MLS）中的运行时不确定性对于维持服务质量（QoS）至关重要。机器学习模型平衡器这一概念通过促进动态ML模型切换来应对这些不确定性，展现出提升MLS中QoS的潜力。基于这一概念，本文介绍了SWITCH——一个旨在通过运行时动态模型切换增强此类系统自适应能力的示例。SWITCH被设计为服务于广泛ML场景的综合性网络服务，并通过目标检测用例展示了其实施。SWITCH为研究人员提供了一个灵活的平台，以应用和评估其ML模型切换策略，旨在提升MLS中的QoS。SWITCH具备高级输入处理、实时数据处理以及适应性指标的日志记录功能，并配有交互式实时仪表板以增强系统可观测性。本文详述了SWITCH的架构、通过ML模型切换实现的自适应策略，并通过案例研究进行了实证验证，展示了其在提升MLS中QoS方面的潜力。通过提供探索ML系统中自适应行为的实践方法，SWITCH为SEAMS社区研究MLS的自适应机制及其实际应用贡献了一个有价值的工具。