How does the DevOps team provide feedback?

Feedback from the DevOps team in machine learning projects usually comes in the form of reports on model performance in production, infrastructure logs, and recommendations for deployment optimization. For example, DevOps might report that a model loads too slowly or consumes too many resources, suggesting the use of lighter model formats or container optimization. There is also often information about failures in data pipelines, which helps ML engineers improve data processing and quality. It is important to maintain close interaction to quickly respond to issues and improve the stability and efficiency of ML systems.