论文标题
使用MLOPS自动部署机器学习模型的连续集成 /连续交付
On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps
论文作者
论文摘要
近年来，机器学习中的模型部署已成为一个有趣的研究领域。它与常规软件开发定义的过程相当。持续集成和连续交付（CI/CD）已显示出与开发和操作（DEVOPS）结合使用时的软件发展和加快业务的速度。另一方面，在包括机器学习操作（MLOP）组件的应用程序中使用CI/CD管道很难解决，并且该区域的先驱者通过使用唯一的工具来解决它们，这通常由云提供商提供。这项研究更深入地了解了机器学习生命周期以及DevOps和MLOPS之间的关键区别。在MLOP方法中，我们讨论了执行机器学习框架CI/CD管道的工具和方法。在此之后，我们深入研究了GitHub操作（GITOPS）中基于推动和拉的部署。还确定并添加了开放勘探问题，这可能指导未来的研究。
Model deployment in machine learning has emerged as an intriguing field of research in recent years. It is comparable to the procedure defined for conventional software development. Continuous Integration and Continuous Delivery (CI/CD) have been shown to smooth down software advancement and speed up businesses when used in conjunction with development and operations (DevOps). Using CI/CD pipelines in an application that includes Machine Learning Operations (MLOps) components, on the other hand, has difficult difficulties, and pioneers in the area solve them by using unique tools, which is typically provided by cloud providers. This research provides a more in-depth look at the machine learning lifecycle and the key distinctions between DevOps and MLOps. In the MLOps approach, we discuss tools and approaches for executing the CI/CD pipeline of machine learning frameworks. Following that, we take a deep look into push and pull-based deployments in Github Operations (GitOps). Open exploration issues are also identified and added, which may guide future study.
