Recently, Machine Learning (ML) has become a widely accepted method for significant progress that is rapidly evolving. Since it employs computational methods to teach machines and produce acceptable answers. The significance of the Machine Learning Operations (MLOps) methods, which can provide acceptable answers for such problems, is examined in this study. To assist in the creation of software that is simple to use, the authors research MLOps methods. To choose the best tool structure for certain projects, the authors also assess the features and operability of various MLOps methods. A total of 22 papers were assessed that attempted to apply the MLOps idea. Finally, the authors admit the scarcity of fully effective MLOps methods based on which advancements can self-regulate by limiting human engagement.

翻译：近期，机器学习（Machine Learning, ML）已成为一种被广泛接受的方法，用于实现快速演进的重大进展，因其采用计算方法来教导机器并生成可接受的答案。本研究探讨了机器学习运维（Machine Learning Operations, MLOps）方法的重要性，这些方法能够为此类问题提供可接受的解决方案。为协助开发易于使用的软件，作者研究了MLOps方法。为针对特定项目选择最优工具架构，作者还评估了不同MLOps方法的特性与可操作性。本研究共评估了22篇尝试应用MLOps理念的论文。最后，作者承认，目前缺乏完全有效的MLOps方法，使得相关进展能够通过限制人工干预来实现自我调节。