Nowadays, Machine Learning (ML) is experiencing tremendous popularity that has never been seen before. The operationalization of ML models is governed by a set of concepts and methods referred to as Machine Learning Operations (MLOps). Nevertheless, researchers, as well as professionals, often focus more on the automation aspect and neglect the continuous deployment and monitoring aspects of MLOps. As a result, there is a lack of continuous learning through the flow of feedback from production to development, causing unexpected model deterioration over time due to concept drifts, particularly when dealing with scarce data. This work explores the complete application of MLOps in the context of scarce data analysis. The paper proposes a new holistic approach to enhance biomedical image analysis. Our method includes: a fingerprinting process that enables selecting the best models, datasets, and model development strategy relative to the image analysis task at hand; an automated model development stage; and a continuous deployment and monitoring process to ensure continuous learning. For preliminary results, we perform a proof of concept for fingerprinting in microscopic image datasets.

翻译：如今，机器学习正经历前所未有的广泛流行。机器学习模型的运营化由一组被称为机器学习运维（MLOps）的概念和方法所管理。然而，研究人员和从业者往往更关注自动化方面，而忽略了MLOps的持续部署与监控环节。这导致缺乏通过生产环境到开发环境的反馈流实现持续学习，进而因概念漂移引发模型性能随时间意外衰退，尤其在处理稀缺数据时尤为突出。本研究探索了MLOps在稀缺数据分析场景中的完整应用，提出了一种新的整体性方法以增强生物医学图像分析。我们的方法包括：一个指纹识别流程，用于根据当前图像分析任务选择最佳模型、数据集及模型开发策略；一个自动化模型开发阶段；以及一个确保持续学习的持续部署与监控流程。作为初步结果，我们在显微图像数据集上进行了指纹识别的概念验证。