论文标题
对GitHub的MLOP实践的初步调查
A Preliminary Investigation of MLOps Practices in GitHub
论文作者
论文摘要
背景。机器学习（ML）应用程序的迅速流行已导致人们对MLOP的兴趣越来越多，即ML启用ML的系统的持续集成和部署（CI/CD）的实践。目标。由于更改不仅可能影响代码，还会影响ML模型参数和数据本身，因此需要扩展传统CI/CD的自动化以管理生产中的模型再培训。方法。在本文中，我们对从GitHub检索的一组启用ML的系统中实施的MLOP实践进行了初步研究，重点是GitHub Action和CML，这是两种解决开发工作流程的解决方案。结果。我们的初步结果表明，在开源GitHub项目中采用MLOPS工作流程目前相当有限。结论。还确定了问题，可以指导未来的研究工作。
Background. The rapid and growing popularity of machine learning (ML) applications has led to an increasing interest in MLOps, that is, the practice of continuous integration and deployment (CI/CD) of ML-enabled systems. Aims. Since changes may affect not only the code but also the ML model parameters and the data themselves, the automation of traditional CI/CD needs to be extended to manage model retraining in production. Method. In this paper, we present an initial investigation of the MLOps practices implemented in a set of ML-enabled systems retrieved from GitHub, focusing on GitHub Actions and CML, two solutions to automate the development workflow. Results. Our preliminary results suggest that the adoption of MLOps workflows in open-source GitHub projects is currently rather limited. Conclusions. Issues are also identified, which can guide future research work.
