02 / CORE
AI Infrastructure Fundamentals
The architecture underneath every production AI platform.
FORMAT4 live instructor-led sessions
LEVELBeginner
DURATION4 sessions
LAB ACCESSIncluded
Who it's for
- · Infrastructure, platform, and DevOps engineers building AI capability on their team
- · SREs who will be on call for GPU workloads
- · Engineers who finished AI Infrastructure 101 and want depth
Prerequisites
- · Basic Kubernetes familiarity
- · Basic Linux administration
What you will build
- · A reference architecture for a private AI platform
- · A working GPU-aware Kubernetes namespace
- · An observability baseline for GPU workloads
What can break
- · Misconfigured GPU device plugins
- · Scheduling constraints that silently strand workloads
CURRICULUM
01
Session 1 — Architecture
- · Mapping the full AI infrastructure stack
- · Where design decisions get made
02
Session 2 — GPU infrastructure
- · Drivers, CUDA, device plugins
- · GPU topology and NVLink basics
03
Session 3 — Networking and storage
- · CNI choices for AI workloads
- · Storage patterns for training and inference
04
Session 4 — Observability and failure modes
- · Metrics that matter for GPU fleets
- · A survey of the ten failure modes
Lab environment
Guided labs on a shared Kubernetes + GPU environment across all four sessions.
Outcomes
- · You can design and defend a reference AI infrastructure architecture
- · You know what to monitor and why before something breaks
Instructor
Built by engineers who have operated infrastructure in the real world.
Mr. Bingi, Founder
Pricing
[Pricing to be announced]
FAQ
Can I take individual sessions?
The course is designed as a 4-session arc, but session-by-session access will be available at signup.
What comes after this?
Most engineers move to GPU Kubernetes or AI Infrastructure Troubleshooting next.