You’ve been asked to move AI model deployment from a manually managed environment to a cloud-native setup so releases are faster, scaling is more reliable, and operations are easier to standardize. The goal is not just to get models running in containers, but to make deployment production-ready across training, serving, monitoring, and incident response.
What are the main challenges of deploying AI models in a cloud-native environment, and how would you approach planning for them?
