Containers for AI services. The default packaging format for production AI.
The container runtime that made microservices viable. Docker packages your code + its dependencies + its runtime into an image that runs identically on any machine. In the bootcamp it underpins everything from week 9 onwards — every capstone ships as a Docker image.
Because the dependency tree of an AI service (CUDA versions, Python wheel pins, system libraries) is brittle. Pin everything in a Dockerfile and you eliminate "works on my machine" entirely — and Kubernetes wants images, not source.
Docker Engine is the dev experience and tooling layer. containerd is the runtime underneath (used directly by Kubernetes). Podman is a daemonless alternative that's rootless by default — useful in restricted environments. For learning, Docker. For prod, your platform decides.
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