A prototype can get away with a messy environment. A production service cannot.

AI applications are especially sensitive to small environment differences. A different Python version, CUDA runtime, PyTorch build, tokenizer package, system library, or model file can change startup behavior, latency, memory use, or whether the service runs at all.

Containers do not make an AI system reliable by themselves. What they give you is a repeatable unit that you can build, scan, deploy, roll back, and debug.

A good container image is reproducible, reasonably small, secure by default, and clear about the CPU, memory, GPU, and model artifacts it needs.

This chapter covers the pieces that make an AI image ready for production: pinned dependencies, runtime secrets, a clear model artifact strategy, health checks, and clear CPU/GPU boundaries.