Kubernetes

Container orchestration for production workloads including ML and LLM inference.

Maturity level: L3 — Can Deploy

Six perspectives on Kubernetes

Roadmap

Learn after Docker and Linux. Core for MLOps, LLMOps, and AI infrastructure paths.

Architecture

Runs application and model workloads. Connects ingress, storage, GPU operators, and observability.

Company

Frequently observed in infrastructure-oriented AI and platform job descriptions (sample-based, not universal).

Projects

Deploy a scalable inference service with HPA, probes, and rolling updates.

Interview

Expect scheduling, networking, probes, autoscaling, and production troubleshooting depth.

Career

Gateway skill from DevOps into MLOps, LLMOps, and AI infrastructure roles.

What & Why

What: Container orchestration platform that schedules, scales, and heals distributed applications.

Why: Runs microservices, ML inference, and data platforms reliably at scale with declarative config.

Build this

Deploy a production ML inference API with autoscaling and health probes.

Production reality

  • ! OOMKilled pods
  • ! CPU throttling
  • ! Bad probes
  • ! ImagePullBackOff
  • ! DNS failures
  • ! PVC problems
  • ! GPU fragmentation
  • ! RBAC issues

Interview preparation

  • Explain pod lifecycle and scheduling decisions
  • How do readiness vs liveness probes differ?
  • Debug a CrashLoopBackOff step by step
  • How would you run GPU workloads on K8s?
  • Describe networking: Service, Ingress, DNS

Failure scenario: OOMKilled Pod

Symptom
Pod restarts repeatedly, status OOMKilled
Root cause
Container memory limit too low for workload (common with ML inference)
Permanent fix
Right-size limits from load testing; add HPA on memory-aware metrics

Connected skills

DockervLLMGPU InfrastructureTerraformPrometheusKServeLinux

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