AI Literacy for Infrastructure Workers
Proposed course specification. Delivery, enrollment, and assessment arrangements require a partner program. Credential references describe intended alignment or pathways; external credentials and academic awards are issued only by the responsible organizations.
This course introduces the vocabulary and physical realities of AI-factory operations to workers entering data-center trades from electrical, mechanical, or general facilities backgrounds. Topics include what a GPU cluster is and why it draws orders of magnitude more power and heat per rack than traditional enterprise compute, the basic training-versus-inference distinction, why liquid cooling has become mandatory rather than optional at current rack densities, and where a facilities worker's job intersects with AI infrastructure (power, cooling, cabling, security) without requiring the worker to program or administer the systems. Students complete a guided tour or digital-twin walk-through of an AI-factory data hall and correctly identify major subsystems.
What you'll be able to do
- Identify the major physical subsystems of an AI-factory data hall (GPU racks, cooling distribution units, power distribution, network fabric) on a facility walkthrough or digital twin.
- Explain why current-generation GPU racks require direct liquid cooling rather than traditional air cooling.
- Distinguish AI model training workloads from inference workloads in terms of duration, resource pattern, and business purpose.
- Locate where a facilities trade (electrical, mechanical, security, cabling) intersects with AI-factory operations in a given job scenario.
- Define core AI-factory vocabulary (GPU, cluster, token, inference, training, liquid cooling, PUE) in plain language.
Train the team that runs the factory.
Shape a program around your facility, your learners, and the competencies employers need.