In recent years, many industries have utilized machine learning models (ML) in their systems. Ideally, machine learning models should be trained on and applied to data from the same distributions. However, the data evolves over time in many application areas, leading to data and concept drift, which in turn causes the performance of the ML models to degrade over time. Therefore, maintaining up to date ML models plays a critical role in the MLOps pipeline. Existing ML model maintenance approaches are often computationally resource intensive, costly, time consuming, and model dependent. Thus, we propose an improved MLOps pipeline, a new model maintenance approach and a Similarity Based Model Reuse (SimReuse) tool to address the challenges of ML model maintenance. We identify seasonal and recurrent distribution patterns in time series datasets throughout a preliminary study. Recurrent distribution patterns enable us to reuse previously trained models for similar distributions in the future, thus avoiding frequent retraining. Then, we integrated the model reuse approach into the MLOps pipeline and proposed our improved MLOps pipeline. Furthermore, we develop SimReuse, a tool to implement the new components of our MLOps pipeline to store models and reuse them for inference of data segments with similar data distributions in the future. Our evaluation results on four time series datasets demonstrate that our model reuse approach can maintain the performance of models while significantly reducing maintenance time and costs. Our model reuse approach achieves ML performance comparable to the best baseline, while being 15 times more efficient in terms of computation time and costs. Therefore, industries and practitioners can benefit from our approach and use our tool to maintain the performance of their ML models in the deployment phase to reduce their maintenance costs.

翻译：近年来，许多行业在其系统中采用了机器学习模型。理想情况下，机器学习模型应在来自相同分布的数据上进行训练并应用于该分布。然而，在许多应用领域中，数据会随时间演变，导致数据和概念漂移，进而使得机器学习模型的性能随时间下降。因此，在MLOps流程中保持模型的最新状态至关重要。现有的机器学习模型维护方法通常计算资源密集、成本高昂、耗时且依赖于具体模型。为此，我们提出了一种改进的MLOps流程、一种新的模型维护方法以及一个基于相似性的模型重用工具，以应对机器学习模型维护的挑战。通过初步研究，我们在时间序列数据集中识别出季节性和周期性分布模式。周期性分布模式使我们能够在未来对相似分布重用先前训练好的模型，从而避免频繁的重新训练。随后，我们将模型重用方法集成到MLOps流程中，提出了改进的MLOps流程。此外，我们开发了SimReuse工具，用于实现MLOps流程中的新组件，以存储模型并在未来对具有相似数据分布的数据片段进行推理时重用它们。我们在四个时间序列数据集上的评估结果表明，我们的模型重用方法能够在保持模型性能的同时，显著减少维护时间和成本。我们的模型重用方法实现了与最佳基线相当的机器学习性能，同时在计算时间和成本上提高了15倍的效率。因此，行业从业者可以从我们的方法中受益，并使用我们的工具在部署阶段保持其机器学习模型的性能，从而降低维护成本。