迈向 MLOps：面向机器学习系统的 DevOps 工具推荐系统
Towards MLOps: A DevOps Tools Recommender System for Machine Learning System
AI总结：
本文针对 MLOps 中开源工具选择困难的问题，提出一种基于项目上下文信息的推荐系统框架，并通过实验证明随机森林方法在四种候选方法中取得最佳推荐效果。
摘要：
将 DevOps 实践应用于机器学习系统被称为 MLOps，与传统系统基于需求演化不同，机器学习系统在新数据上持续演化。MLOps 的目标是在不同的开源工具之间建立连接，以构建一条能够自动执行数据集构建、机器学习模型训练、模型部署到生产环境以及存储模型和数据集不同版本的流水线。MLOps 的益处在于确保新训练的模型能够快速交付到生产环境，从而获得准确的结果。此外，MLOps 实践会影响软件产品的整体质量，并且完全依赖于开源工具，而相关开源工具的选择被认为是一项挑战，因此需要一种通用方法来选择适当的开源工具。在本文中，我们提出了一个推荐系统框架，该框架处理机器学习项目的上下文信息（例如数据性质、数据类型），并为机器学习系统的运维化推荐相关的工具链（技术栈）。为了检验所提出框架的适用性，我们研究了四种不同的方法，即基于规则的方法、随机森林、决策树和 k 近邻，并测量了精确率、召回率和 f-score，其中随机森林以 0.66 的最高 f-score 值优于其他方法。
英文摘要：
Applying DevOps practices to machine learning system is termed as MLOps and machine learning systems evolve on new data unlike traditional systems on requirements. The objective of MLOps is to establish a connection between different open-source tools to construct a pipeline that can automatically perform steps to construct a dataset, train the machine learning model and deploy the model to the production as well as store different versions of model and dataset. Benefits of MLOps is to make sure the fast delivery of the new trained models to the production to have accurate results. Furthermore, MLOps practice impacts the overall quality of the software products and is completely dependent on open-source tools and selection of relevant open-source tools is considered as challenged while a generalized method to select an appropriate open-source tools is desirable. In this paper, we present a framework for recommendation system that processes the contextual information (e.g., nature of data, type of the data) of the machine learning project and recommends a relevant toolchain (tech-stack) for the operationalization of machine learning systems. To check the applicability of the proposed framework, four different approaches i.e., rule-based, random forest, decision trees and k-nearest neighbors were investigated where precision, recall and f-score is measured, the random forest out classed other approaches with highest f-score value of 0.66.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
