How I'd Learn AI Engineering From Zero in 2026 (1 hour masterclass)

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Learn all AI engineering skills in one place with Data Camps hand on tracks:
1️⃣ Associate AI Engineer course for devs: https://datacamp.pxf.io/qWVJQj
2️⃣ Associate AI Engineer course for data scientists: https://datacamp.pxf.io/PzBXeX

AI is moving insanely fast, and most people are still only using a tiny fraction of what these tools can actually do.

In this video, I break down the realistic roadmap I would follow if I had to learn AI engineering from zero in 2026 — not just how to use ChatGPT, but how to build real AI systems, work with agents, understand modern coding tools, and ship production-ready software with AI in your workflow.

We'll cover the foundations you need first, including TypeScript, Next.js, Expo, Python, APIs, databases, architecture, and the programming patterns that help you actually understand the code AI writes for you.

Then we go deeper into the AI engineering side: harnesses, agents, planning, context windows, model choice, MCPs, skills, plugins, memory, RAG, vector databases, automation, Docker, CI/CD, Linear, and the legal/data considerations you cannot ignore when shipping real products.

We'll cover:
✅ What an AI engineer actually does in 2026
✅ The coding foundations I would learn first
✅ How to read, understand, and review AI-generated code
✅ The difference between harnesses, agents, models, MCPs, skills, and plugins
✅ How to control AI costs with better planning, model choice, caching, and context management
✅ How memory, RAG, embeddings, and vector databases make AI systems smarter
✅ How to run agents safely with sandboxing, permissions, and isolated environments
✅ The production skills most beginners skip: Docker, CI/CD, Linear, legal, data privacy, and feedback loops

This is the roadmap I wish more people followed before calling themselves AI engineers — because the real skill is not just vibe coding an app, it's building systems that are useful, safe, scalable, and actually shippable.

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