论文标题
机器学习操作：有关MLOPS工具支持的调查
Machine Learning Operations: A Survey on MLOps Tool Support
论文作者
论文摘要
机器学习（ML）已成为实践中解决方案开发中一种快速增长的趋势方法。深度学习（DL）是ML的子集，它使用深层神经网络学习模拟人脑。它使用计算机算法训练机器以分别学习技术和过程，这也被认为是人工智能（AI）的作用。在本文中，我们研究了与在ML项目上工作的组织中与软件开发和交付有关的当前技术问题。因此，讨论了机器学习操作（MLOP）概念的重要性，可以为此类关注提供适当的解决方案。我们研究了软件开发中的市售MLOPS工具支持。 MLOP工具之间的比较分析了每个系统及其用例的性能。此外，我们研究了MLOP工具的功能和可用性，以确定给定情况的最合适的工具支持。最后，我们认识到，功能齐全的MLOP平台的可用性短缺，可以通过减少人类干预来自动化过程。
Machine Learning (ML) has become a fast-growing, trending approach in solution development in practice. Deep Learning (DL) which is a subset of ML, learns using deep neural networks to simulate the human brain. It trains machines to learn techniques and processes individually using computer algorithms, which is also considered to be a role of Artificial Intelligence (AI). In this paper, we study current technical issues related to software development and delivery in organizations that work on ML projects. Therefore, the importance of the Machine Learning Operations (MLOps) concept, which can deliver appropriate solutions for such concerns, is discussed. We investigate commercially available MLOps tool support in software development. The comparison between MLOps tools analyzes the performance of each system and its use cases. Moreover, we examine the features and usability of MLOps tools to identify the most appropriate tool support for given scenarios. Finally, we recognize that there is a shortage in the availability of a fully functional MLOps platform on which processes can be automated by reducing human intervention.
