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AI Infrastructure 101
Understand what actually happens between a model and the hardware running it.
FORMAT90-minute live online session
LEVELBeginner
DURATION90 minutes
LAB ACCESSIncluded
Who it's for
- · Engineers who keep hearing about "AI infrastructure" and want the real map
- · Linux, cloud, and platform engineers evaluating whether to specialize
- · Anyone tired of tutorials that start at "pip install" and skip the hardware
Prerequisites
- · Comfortable with a Linux shell
- · No GPU or ML experience required
What you will build
- · A mental model of the full stack from workload to datacenter
- · A working vocabulary: CUDA, device plugins, schedulers, RDMA, tensor parallelism
What can break
- · Nothing — this session is diagnostic, not destructive
- · You will see real failure examples, not cause them
CURRICULUM
01
The anatomy of an AI workload
- · What a training job and an inference request actually do on a machine
- · Where CPU work ends and GPU work begins
02
The hardware and runtime layer
- · CUDA and the driver stack
- · Containers and GPU passthrough basics
03
The orchestration layer
- · Kubernetes GPU scheduling in plain terms
- · Where OpenStack and bare metal fit underneath
04
The parts that break
- · A tour of the ten failure modes you will meet across this catalog
Lab environment
Live walkthrough of a real GPU cluster. No hands-on lab in this session.
Outcomes
- · You can explain the AI infrastructure stack to another engineer, end to end
- · You know which course in this catalog matches where you sit today
Instructor
Built by engineers who have operated infrastructure in the real world.
Mr. Bingi, Founder
Pricing
[Pricing to be announced]
FAQ
Do I need GPU experience?
No. This session is built for infrastructure engineers who have not worked with GPUs yet.
Is this recorded?
Live sessions are the primary format. Recording availability will be confirmed at signup.