The rise of digital transformation and the widespread use of machine learning in software production processes have led to the emergence of two key paradigms in modern development: DevOps and MLOps. Both approaches are fundamentally built on collaboration, continuous communication, and synergy among multidisciplinary teams; however, they differ significantly in their goals, tools, and communication requirements.

This study adopts a systematic literature review approach, analyzing 60 credible sources published between 2015 and 2025 to examine the structures of collaboration, communication models, and cultural challenges in implementing DevOps and MLOps. Findings reveal that DevOps primarily focuses on automation, rapid feedback, and horizontal communication between development and operations teams, whereas MLOps places greater emphasis on coordination among data scientists, machine learning engineers, and production teams.

The research highlights that the lack of a shared language, differences in modeling and deployment cycles, and the absence of unified communication tools are among the main barriers to effective collaboration in both cultures. Nevertheless, adopting integrated data frameworks, creating cross-functional roles such as DevOps/MLOps Evangelists, and promoting shared cultural values can enhance synergy and efficiency.

Finally, the paper proposes a conceptual framework aimed at improving inter-team communication within DevOps and MLOps environments, offering a foundation for future research and the development of organizational solutions.