Become an AI Infrastructure Engineer with real Kubernetes and real GPUs
Run GPU clusters that don't melt down. Kubernetes, GPU Operator, MIG, MPS, observability.
About this path
AI Infrastructure Engineers run the GPU clusters that everyone else's models depend on. The role lives at the intersection of Kubernetes platform engineering and GPU-specific operations, and almost no online course teaches it through real clusters. This path does. Every module is a lab on a live, isolated Kubernetes environment with real GPU scheduling, real GPU operator components, and real triage scenarios that break the way they break in production.
Skills you'll put on a resume
- Schedule GPU workloads on Kubernetes with the right resource requests, limits, and priority classes
- Install and triage the NVIDIA GPU Operator end-to-end, attributing every component's role
- Share single GPUs across workloads with CUDA streams, MPS, and MIG, and know when each fits
- Implement the four-stage cost-audit pipeline (measure → classify → price → recommend) for GPU fleets
- Profile PyTorch training with Nsight Systems and the built-in profiler to find real bottlenecks
- Pass the NVIDIA-Certified Associate AI Infrastructure & Operations exam
For
Platform engineers, SREs, and DevOps engineers responsible for running GPU workloads in production. Comfortable with Linux and Kubernetes basics; new to GPU-specific operations.
Prerequisites
- Comfortable with Linux command line and basic shell scripting
- Kubernetes basics (Pods, Deployments, Services), equivalent of CKA prep
- Familiarity with containers (Docker / containerd)
Guides & articles
Deep-dive reading that pairs with this course
Ready to start?
Pro gives you all 16 labs in this path, every other lab on Preporato, and every practice test. $29.99/mo, cancel anytime.