DataKern (also written Data Kern) was created to close the gap between theoretical learning and the engineering capability needed to build real Data & AI systems.
Uma Kiran Ramadugu
Data & AI Architect · DataKern Founder
15+ years in IT
Enterprise systems and data platforms
12+ years in Data & AI
Pipelines, platforms and AI systems
Today
Data & AI Architect at a bank in the Netherlands
I kept seeing the same thing. Engineers finished courses and got certificates. But they couldn't explain what they had built. In interviews, they struggled to defend their choices.
Today, AI writes code too. With a good prompt, almost anyone can build almost anything. But many people can't say why it works, or whether it will still work in production next week or next month. AI doesn't own production. You do.
I believe it doesn't matter who writes the code. It can be you or AI. What matters is why. Why is it built this way? Is this the right design?
That is what I teach: how the pieces fit together. Once you understand that, you can judge any code.
That's why I started DataKern. Small batches. Live sessions. Real projects. And direct feedback on how you think, not just what you built.
— Uma Kiran Ramadugu, Founder
Data & AI Architect · DataKern Founder
15 years in IT. 12 years in Data & AI. Currently working as a Data & AI Architect at a bank in the Netherlands — building the same kinds of systems I teach.
My experience spans enterprise data platforms, cloud migrations, banking-grade data pipelines, AI application design, and team architecture reviews. Not theoretical — I work in production environments every day.
Teaching is not a side project. It's how I make sense of what I've learned, and how I help others avoid the mistakes I made the hard way.
Who writes the code does not matter. Why it is written that way does.
You can write it, a teammate can, or AI can. What counts is understanding why it is built this way, and whether it is the right design.
Projects should be explainable
Running is not enough. Can you walk a senior engineer through every decision, without notes?
Live feedback changes how you learn
You can watch a hundred videos. You improve when someone who knows the work reviews yours and tells you what they see.
AI makes data foundations more important
Not less. Every AI application depends on clean, reliable data. Good data engineering matters more now, not less.
Proof should show judgment, not memory
A certificate that tests what you remember says you studied. We test what you can decide: choose, check and explain.
Prove what you can decide, not what you can memorise. Each exam is a working day at a fictional company. You read the situation, then choose, check and explain, the way an engineer does.
It is open-book and covers Data Engineering and AI Engineering, at Associate and Professional levels. Pass, and you get a badge anyone can verify on data-kern.com.
It is DataKern's own credential, not a Microsoft, Databricks or other vendor certification.
Explore the certification →We are designing internships for outstanding graduates to work on real DataKern projects.
Details coming soon.
Data Engineering and AI Engineering batches are in progress. Applications are open for the January 2027 batch.