The accelerated adoption of AI-based software demands precise development guidelines to guarantee reliability, scalability, and ethical compliance. MLOps (Machine Learning and Operations) guidelines have emerged as the principal reference in this field, paving the way for the development of high-level automated tools and applications. Despite the introduction of MLOps guidelines, there is still a degree of skepticism surrounding their implementation, with a gradual adoption rate across many companies. In certain instances, a lack of awareness about MLOps has resulted in organizations adopting similar approaches unintentionally, frequently without a comprehensive understanding of the associated best practices and principles. The objective of this study is to gain insight into the actual adoption of MLOps (or comparable) guidelines in different business contexts. To this end, we surveyed practitioners representing a range of business environments to understand how MLOps is adopted and perceived in their companies. The results of this survey also shed light on other pertinent aspects related to the advantages and challenges of these guidelines, the learning curve associated with them, and the future trends that can be derived from this information. This study aims to provide deeper insight into MLOps and its impact on the next phase of innovation in machine learning. By doing so, we aim to lay the foundation for more efficient, reliable, and creative AI applications in the future.

翻译：基于人工智能的软件加速采用，需要精确的开发指南来保证其可靠性、可扩展性及合规性。MLOps（机器学习与运维）指南已成为该领域的主要参考标准，为开发高级自动化工具与应用铺平了道路。尽管MLOps指南已被提出，但其实际实施仍存在一定程度的质疑，许多企业的采用进程也较为缓慢。在某些情况下，对MLOps缺乏认知导致组织无意中采用了类似的方法，却往往未能全面理解相关的最佳实践与原则。本研究旨在深入了解MLOps（或类似）指南在不同商业环境中的实际采用情况。为此，我们调查了代表多种商业环境的从业者，以了解其所在公司对MLOps的采用方式与认知态度。调查结果还揭示了与这些指南相关的优势、挑战、学习曲线以及可从中推导的未来趋势等其他重要方面。本研究旨在更深入地理解MLOps及其对机器学习下一阶段创新的影响，从而为未来开发更高效、可靠且富有创造力的人工智能应用奠定基础。