Data Center Engineer

Starts October 17

Online

Track

Understand how an AI data center works from substation to GPU and learn to calculate how much capacity a campus can really deliver under load, and what limits it.

Private Discord community

Practice, quizzes, and a final project

Mentors and checkpoints

Lifetime access and updates

TrackUnderstand why announced megawatts are not live megawatts and what limits an AI campus: power and redundancy, dense GPU racks and 800 VDC, liquid cooling and heat rejection in hot weather, the building and the rack route, the cluster network and useful GPU work, grid interconnection, water and noise, operations, schedule and money. Every exercise uses one training campus, Kelvin-1: complete data, calculator work and a step-by-step answer that explains the typical wrong results. No engineering background or programming is required; school physics and percentages are enough. In the final project you review a real AI campus from the news and reach a verdict on the operator's main claim.

Convert any capacity claim to one basis: IT or grid, through PUE, nameplate or live
Check the power path under maintenance and failure: N+1, 2N, UPS and the transfer to generators
Calculate the GPU rack and hall budget: kW per rack, liquid and air, CDU with redundancy, weight
Find the summer IT capacity limit and the water needed on a peak day
Read the cluster network: fabrics, oversubscription, all-reduce time and counter-based triage
Estimate training lost to failures and the checkpoint interval, and run an incident by the runbook
Price a month of delay and the launch critical path, then review a real AI campus

Data Center Engineer

Who this is for

New

People who want to join an AI data center team as a capacity analyst or launch coordinator

Pro

Electrical, HVAC and IT engineers who need the AI-specific picture

Operations teams that run incidents and read telemetry

Analysts and investors who need to tell slide megawatts from live ones

Readers of gigawatt headlines who want to do the math themselves

Have questions?

We’ll reply within 15 minutes

Or contact

a consultant directly

Curriculum

Eleven modules follow the path of a megawatt: capacity and power, the rack, cooling, the building and the network, then the grid, operations, schedule and a final campus review.

Data Center Capacity

Power

GPU Rack

Cooling

Building

Learning Platform

Short lessons, practice, and final artifacts live on one platform. Wallet login does not require a separate password.

Wallet Login
Connect MetaMask from any device and resume where you stopped. You do not need a separate password.
Short Lessons
Lessons are split into small blocks. Complete one in a short session, then resume from the same point later.
Practice Throughout
Assignments apply the material right after a lesson. In larger programs, separate outputs build toward a final project or capstone.
One Workspace
Lessons, quizzes, assignments, and course materials stay together. Technical tasks can use the editor and syntax highlighting.

CommunityEach block ends with an assignment. Keep the intermediate results and assemble the final piece step by step.

Reviews and Community

Practice Reviews

Mentors review code, models, product decisions, and research artifacts in a format that fits each program.

Expert AMAs

Research Briefings

What You Leave With

Finish the program with a verifiable result you can show to a team or use in your next project.

Final Project

Build the program's main result against clear criteria and take it to a state another person can verify.

Portfolio of Work

Keep intermediate artifacts, sources, and decisions so others can see how you worked and why you reached the result.

Next Step

Record the result's limits, open questions, and next actions for your own project, a team, or a new role.