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.
翻译:深度学习(DL)是ML的子集,它学习使用深神经网络模拟人类大脑,它培训机器使用计算机算法学习技术和过程,计算机算法也被认为是人工智能(AI)的作用。在本文中,我们研究了从事ML项目工作的组织目前与软件开发和交付有关的技术问题。因此,讨论了机器学习操作(MLOPs)概念的重要性,它能够为这类关切提供适当的解决办法。我们调查了在软件开发中商业上可获得的MLOPs工具支持。对MLOPs工具的比较分析了每个系统及其使用案例的性能。此外,我们研究了MLOPs工具的特性和可用性,以确定对特定情景的最适当工具支持。最后,我们认识到缺乏一个功能齐全的MLOPs平台,通过减少人类干预可以实现流程自动化。