Organizational efforts to utilize and operationalize artificial intelligence (AI) are often accompanied by substantial challenges, including scalability, maintenance, and coordination across teams. In response, the concept of Machine Learning Operations (MLOps) has emerged as a set of best practices that integrate software engineering principles with the unique demands of managing the ML lifecycle. Yet, empirical evidence on whether and how these practices support users in developing and operationalizing AI applications remains limited. To address this gap, this study analyzes over 8,000 user reviews of AI development platforms from G2.com. Using zero-shot classification, we measure review sentiment toward nine established MLOps practices, including continuous integration and delivery (CI/CD), workflow orchestration, reproducibility, versioning, collaboration, and monitoring. Seven of the nine practices show a significant positive relationship with user satisfaction, suggesting that effective MLOps implementation contributes tangible value to AI development. However, organizational context also matters: reviewers from small firms discuss certain MLOps practices less frequently, suggesting that organizational context influences the prevalence and salience of MLOps, though firm size does not moderate the MLOps-satisfaction link. This indicates that once applied, MLOps practices are perceived as universally beneficial across organizational settings.

翻译：组织在利用和运维化人工智能（AI）的过程中常面临诸多重大挑战，包括可扩展性、系统维护及跨团队协作等。为此，机器学习运维（MLOps）作为一套最佳实践应运而生，它将软件工程原则与机器学习生命周期管理的特殊需求相结合。然而，关于这些实践是否以及如何支持用户开发和运维化AI应用的实证证据仍然有限。为填补这一空白，本研究分析了来自G2.com平台的8000余条AI开发平台用户评论。通过零样本分类方法，我们量化了用户对九项成熟MLOps实践的情感倾向，包括持续集成与持续交付（CI/CD）、工作流编排、可复现性、版本控制、协作机制及监控体系。其中七项实践与用户满意度呈现显著正相关，表明有效的MLOps实施能为AI开发创造实际价值。但组织情境同样重要：来自小型企业的评论者对某些MLOps实践的讨论频率较低，这暗示组织情境会影响MLOps的普及度和关注度，不过企业规模并未调节MLOps与满意度之间的关联。这表明MLOps实践一旦得到应用，在不同组织环境中均被视为具有普遍效益。