We Teach People to Build AI.
Founded in 2021 by engineers who got tired of theory-heavy courses that left students unprepared for actual jobs. We built the program we wished we'd had.
Our Mission
Most AI courses teach you to pass exams. We teach you to ship products. The world has enough people who can explain gradient descent on a whiteboard. It needs more people who can debug a production model at 3 AM.
We're building a learning experience that mirrors how you'll actually work: messy datasets, unclear requirements, and real deadlines. Because that's what you'll face in your job.
Our Journey
Four years of iteration, thousands of students, one clear philosophy.
Foundation
Launched with 12 students and a single course. Used a rented conference room and lots of coffee. Everyone passed, three got job offers before graduation.
Expansion
Grew to 200 students across four cohorts. Added mentorship program after student feedback. Moved to dedicated office space in Ramsgate.
Recognition
Reached 1,000 alumni. Featured in TechCrunch for placement rates. Launched corporate training program for three Fortune 500 companies.
Scale
Trained 2,400+ students from 50 countries. Introduced advanced specialization tracks. Maintained small cohort sizes despite demand.
Innovation
Launched AI lab for student experiments. Added research track for advanced learners. Still learning, still improving.
Meet the Team
We're not academics. We're builders who happen to teach.
James Thompson
Founder & Lead Instructor
Spent eight years at DeepMind and Google Brain before starting Wabi Tech. Published 15 papers on neural architecture search, but prouder of the 2,000 students who've launched AI careers. Still codes every day.
Dr. Elena Martinez
NLP Instructor
Former research scientist at Stanford and OpenAI. Led development of language models used by millions. Joined Wabi Tech to work directly with learners instead of through papers. Believes the best way to understand transformers is to break them.
Raj Patel
Computer Vision Lead
Built autonomous systems at Tesla for five years. Holds 12 patents in object detection and tracking. Teaches students to ship CV models that work on real hardware, not just benchmarks. Known for ruthlessly cutting unnecessary complexity.
Sarah Kim
MLOps & Deployment
Scaled ML infrastructure at Uber and Netflix. Expert in making models survive production traffic. Teaches the unglamorous but critical skills: monitoring, versioning, and incident response. Former bootcamp student turned instructor.
How We Teach
Project-first, theory-when-needed, always applicable.
Start with the Problem
Every module begins with a real scenario. Not "let's learn about CNNs," but "this medical imaging startup needs to classify X-rays." You'll understand why before what.
Build Messily, Refine Iteratively
Your first solution will be ugly. That's fine. You'll refactor it in week two. Then optimize in week three. That's how real development works.
Theory on Demand
We explain the math when you need it, not upfront. Hit a performance ceiling? Now let's talk about learning rates. Confused about architecture choices? Time for theory.
Ship to Production
Every project ends with deployment. You'll set up APIs, write documentation, and handle edge cases. Because a model in a notebook isn't useful to anyone.
This approach takes longer. Students sometimes get frustrated. But they leave prepared.
Experience It YourselfWhat We Believe
Practice Over Theory
Understanding comes from building, not memorizing equations.
Real Over Polished
We show you the mistakes, dead ends, and debugging sessions.
Small Over Scalable
We could teach thousands. We choose dozens so everyone gets attention.
Honest Over Hype
AI can't do everything. We'll tell you what works and what doesn't.
Visit or Reach Us
Our office is open for student visits by appointment. Coffee's always on.