How does a model server typically collaborate with data scientists and engineers during model deployment?

Model Servers play a key role in bridging the gap between data science teams and engineering teams during model deployment. They are responsible for integrating trained machine learning models into production environments, ensuring scalability, reliability, and low-latency access. Collaboration often involves regular communication with data scientists to understand model requirements and with engineers to align on infrastructure, security, and monitoring needs. This teamwork helps ensure that deployed models perform well and can be efficiently maintained or updated as new versions are released.