06 / FLAGSHIP
Production AI Infrastructure
The full stack, end to end, run the way production actually runs.
FORMAT8 live instructor-led sessions + Failure Lab access
LEVELAdvanced
DURATION8 sessions
LAB ACCESSIncluded
Who it's for
- · Infrastructure architects designing a private AI platform
- · Senior platform and SRE engineers responsible for production AI infrastructure
Prerequisites
- · GPU Kubernetes or equivalent experience
- · Production operations experience in Linux/Kubernetes environments
What you will build
- · A production-grade AI infrastructure stack: bare metal through multi-tenant inference
- · Security and isolation boundaries for shared GPU infrastructure
- · An operational runbook for the platform you build
What can break
- · Every layer of the stack — this course integrates the full Failure Lab catalog into a single environment
CURRICULUM
01
Foundation
- · Bare metal, Linux, GPU drivers, CUDA
02
Runtime and orchestration
- · Container runtime
- · Kubernetes and Slurm side by side
03
Platform services
- · GPU scheduling
- · Networking and storage at scale
04
Operations
- · Observability
- · Multi-tenancy and security
- · Inference and training workloads in production
Lab environment
Full Failure Lab access across a production-shaped, multi-tenant GPU environment.
Outcomes
- · You can design, build, and operate a production AI infrastructure platform
- · You can defend every architectural decision under operational pressure
Instructor
Built by engineers who have operated infrastructure in the real world.
Mr. Bingi, Founder
LIVE COHORTS
Scheduled across US, Europe, India, Japan, and Middle East.
No cohorts are currently scheduled for this course. Join a Break My Cluster session in the meantime, or reach out and we'll let you know as soon as dates are announced.
FAQ
Is this the right starting point?
No — this is the capstone. Most engineers arrive here through GPU Kubernetes or AI Infrastructure Fundamentals.
Is Slurm required?
Slurm is covered alongside Kubernetes for teams running HPC-style training workloads.