Title: Discussion 1 — systemd vs. Containers and Orchestrators
Course: AIINFRA 200: Production Inference Serving & GPU Orchestration > Section: Week 01 — Linux Server Foundations for AI Workloads

💬 Discussion 1 — systemd vs. Containers and Orchestrators (20 pts)

This week you managed a long-running process directly with systemd on a bare-metal (or bare-VM) Linux server. In AIINFRA 101 you learned Docker and Kubernetes basics, which offer a different way to manage long-running processes — container runtimes and orchestration platforms. Prompt: In your own words, compare the systemd service-management approach to using a container runtime (like Docker) or a full orchestration platform (like Kubernetes) to manage a model-serving process. Consider:

- What does systemd give you "for free" that you'd otherwise have to reimplement in a container/orchestration setup (or vice versa)?
- Are there situations where plain systemd on bare metal is still the right choice for serving an AI model in production, rather than always reaching for Kubernetes? What are they?
- Based on this week's lab, what was one thing that felt harder (or easier) to manage with systemd directly compared to what you remember from Docker/Kubernetes?

Post your initial response (150+ words), then respond to at least two classmates, engaging with a specific point they made rather than just agreeing.