A camp is a scheduled, instructor-led run through a role: live sessions on a fixed calendar, with the same real virtual machines the self-paced labs use. You keep access to the lab environment between sessions and work the same hardware problems the job actually puts in front of you.
Live camps are $99.99/mo. Self-paced access to every training track is $14.99/mo. You pay any certification vendor directly for the official exam; a subscription covers preparation, not exam fees.
26 weeks. 52 sessions. Real hardware. Real skills.
Hands-on labs on real hardware. BMC, Linux, automation, troubleshooting, and a capstone project you can walk into an interview with. June group in progress, December group open.
26 weeks | 2 sessions per week, 3 hours each (6 hours total) | $99.99/mo
Deep-dive into Linux systems administration.
Linux internals, virtualization, networking services, configuration management, and scripting. For those who want to own the operating system from the kernel up.
16 weeks | $99.99/mo
Reliability as a discipline.
Grafana, Prometheus, ArgoCD, Kubernetes, performance tuning, and advanced Linux. For engineers who make systems resilient at scale.
20 weeks | $99.99/mo
Build the systems that run the AI boom.
The AI boom is not about models. It is about the infrastructure that trains and serves them. We train engineers to build and run GPU clusters, AI fabric networking, and distributed training platforms at the scale that frontier labs and GPU clouds require.
$99.99/mo
Deploy, monitor, and scale AI agents in production.
The companies deploying AI agents need operators who understand rate limiting, failure recovery, observability, and sandboxing at scale. Nobody teaches this. We will be first.
14 weeks | $99.99/mo
Run the systems that serve every AI model in production.
Every AI product that users interact with runs through an inference stack. vLLM, Triton, TGI, autoscaling, batching, quantization, latency SLOs. This is the fastest-growing infrastructure segment through 2030.
16 weeks | $99.99/mo
Orchestrate GPU workloads across clusters at scale.
Standard Kubernetes training stops at deployments and services. AI workloads need GPU-aware scheduling, topology affinity, job queuing, multi-cluster federation, and model artifact pipelines. This is premium-tier K8s.
18 weeks | $99.99/mo
The diagnostic skills that keep GPU clusters running.
XID errors, ECC failures, PCIe link degradation, NVLink topology issues, thermal throttling. These are hands-on diagnostic skills that cannot be automated away. When a 1,000-GPU cluster drops nodes, someone has to find out why.
12 weeks | $149.99/mo
Design and operate the fabric that connects GPU clusters.
InfiniBand, RoCE v2, Spectrum-X, NCCL topology optimization, GPUDirect RDMA. The networking layer that makes distributed AI training possible. Niche skillset, extreme demand, premium compensation.
14 weeks | $199.99/mo
Browse the self-paced training tracks if you would rather work through the material on your own schedule.