Build the talent that keeps AI infrastructure working at full potential. AI infrastructure investment has crossed $500B in annual capex by 2026. The constraint is no longer hardware - it's people.

We train engineers to diagnose underutilized GPUs, fix scheduling pathologies, and keep production clusters stable, saturated, and cost-efficient.

Capital arrived first. Skills didn't. GPUs sit idle not due to lack of demand, but due to a shortage of engineers who can keep clusters saturated, stable, and outcome-driven.

AI infrastructure is mission-critical. Every idle GPU minute means lost time and wasted capital. The need of the hour is to ensure GPUs stay busy, systems remain predictable, and teams operate with the right skills and attitude.

• Built by practitioners
• Focused on day-to-day operations and real production patterns
• Certification readiness with context, plus the vocabulary teams expect on the job and in interviews

Platform engineers, SREs, infrastructure engineers, and architects responsible for keeping AI clusters performant, predictable, and cost-efficient in production.

Who This Is Not For

This is not introductory ML training, data science coursework, or "AI 101." We assume comfort with Linux, networking, containers, and production systems.

A community aligned around a single objective: make AI infrastructure work - consistently, efficiently, and at scale. Through monthly free webinars and focused training, we help engineers get ahead - early.

The infrastructure is ready. Now the engineers must operate it.