Data is becoming more complex, and so are the approaches designed to process it. Enterprises have access to more data than ever, but many still struggle to glean the full potential of insights from what they have. This research explores the challenges and experiences of Iranian developers in implementing the MLOps paradigm within enterprise settings. MLOps, or Machine Learning Operations, is a discipline focused on automating the continuous delivery of machine learning models. In this study, we review the most popular MLOps tools used by leading technology enterprises. Additionally, we present the results of a questionnaire answered by over 110 Iranian Machine Learning experts and Software Developers, shedding light on MLOps tools and the primary obstacles faced. The findings reveal that data quality problems, a lack of resources, and difficulties in model deployment are among the primary challenges faced by practitioners. Collaboration between ML, DevOps, Ops, and Science teams is seen as a pivotal challenge in implementing MLOps effectively.

翻译：数据正变得日益复杂，处理数据的方法亦是如此。企业拥有比以往更多的数据，但许多企业仍难以充分挖掘其中蕴含的洞察潜力。本研究探讨了伊朗开发者在企业环境中实施MLOps范式时所面临的挑战与实践经验。MLOps（机器学习运维）是一门专注于自动化机器学习模型持续交付的学科。在本研究中，我们回顾了领先科技企业使用的最流行的MLOps工具。此外，我们展示了一项由超过110名伊朗机器学习专家和软件开发人员完成的问卷调查结果，揭示了MLOps工具及面临的主要障碍。调查结果表明，数据质量问题、资源匮乏以及模型部署困难是实践者面临的主要挑战。机器学习、DevOps、运维与科学团队之间的协作被视为有效实施MLOps的关键挑战。