95% of AI courses are teaching you the wrong things.

They focus on theory.

Real AI engineers need deployment skills.

Only 3 courses get this right:

1️⃣ DataCamp's AI Engineer Track ($39/month)

While everyone's learning transformers...

This teaches you MLOps.

What I built after week 2:

→ Models that don't crash at 3am

→ Rate limiting that saved me $2K

→ Actual deployment pipelines

The kicker? It's browser-based.

No setup. Just ship.

2️⃣ UC Berkeley's LLM Agents (FREE)

Google engineers teaching for free.

30,000 students in Discord.

Same content as their $70K program.

But here's the catch:

It's HARD. Like, really hard.

Assumes you can already code.

Most people quit by week 3.

3️⃣ HuggingFace's LLM + Agents + MCP Courses (FREE)

The trifecta nobody talks about:

→ LLM Course: Actually understand what you're building

→ Agents Course: Build systems that think

→ MCP Course with Anthropic: Connect to real data

Updated weekly. Community-driven.

The Discord alone is worth gold.

🚨 The Uncomfortable Truth:

Everyone wants to be an "AI Engineer."

Nobody wants to learn Docker, Kubernetes, or rate limiting.

But guess what?

Your beautiful model is useless if it can't handle production traffic.

I spent 6 months on theory courses.

Then I took these 3.

The difference?

Theory courses teach you to think.

These teach you to ship.

What's your take, am I being too harsh on theory?

P.S. If your AI course doesn't include error handling, monitoring, and production deployment... you're learning to build demos, not products.