00 - Where are you starting from

What this session is

Ten minutes. Honest self-assessment. Determines which pages of this roadmap actually apply to you and which you can skip.

The four starting points

People show up to "I want to be an AI engineer" from very different places. The advice for each is different. Find yours.

A. Never coded

You've used ChatGPT. You've read articles. You've never written a for loop. You're not sure if you're "the kind of person" who codes.

Reality: 12-18 months. Most of it is just becoming a programmer. The AI-specific layer is the last 4-6 months.

Start with: Python from Scratch on this site. Finish all 16 pages, do every exercise, get one PR merged. Then come back here.

B. Coded a bit (HTML/CSS, Excel macros, a Python tutorial)

You can write a script that prints something. Loops, ifs, basic data structures are familiar in some language but you're shaky.

Reality: 9-12 months. Skip the absolute basics, but reinforce - there's a difference between "did the tutorial" and "could rebuild it from memory."

Start with: Finish the back half of Python from Scratch. Read this roadmap. Then start AI Systems from Scratch.

C. Working programmer, not in ML

Backend, frontend, SRE, data engineering, whatever. You ship code for a living. You haven't trained a model.

Reality: 6-9 months. The hardest transition isn't programming - it's the math, the model intuition, and learning to think in tensors.

Start with: Skim Python from Scratch (1 hour). Read this roadmap. Spend most time in AI Systems from Scratch and the senior AI Systems path.

D. ML-adjacent (data scientist, ML researcher, analyst)

You've trained models. You've read papers. You're trying to move from notebooks to production AI engineering.

Reality: 3-6 months. The gap is engineering: serving, infra, evaluation in production, cost, latency.

Start with: Read this roadmap quickly. Skip ahead to Picking a specialization. Spend most time in serving / infra / OSS contribution.

The honest test

Answer these out loud:

1. Can you, right now, write a Python script that reads a file, counts the words, and prints the top 10 most common?
2. Do you know what a function, a class, and a list comprehension are without looking them up?
3. Have you ever installed a Python library with pip and used it?

4. Have you ever used a Linux terminal for anything beyond cd?

5. 0-1 yes → bucket A.

6. 2 yes → bucket B.
7. 3 yes → bucket C (lean B if shaky).
8. 4 yes + can train a model → bucket D.

What this means for the rest of this path

The rest of these 16 pages assume bucket C - a working programmer moving into AI. Bucket A/B readers: complete the prerequisite paths first, then come back. Bucket D readers: the orientation pages will feel obvious; the strategy pages (specialization, jobs, portfolio) are the value.

What you might wonder

"Can I skip programming and just learn 'AI'?" No. The job is "AI engineer." The first word is descriptive; the second is the role. Engineers code.

"What about no-code AI tools?" Different career. Useful skill, but not what hiring managers mean by "AI engineer."

"I'm 40 / 50 / changing careers. Too late?" No. The field is 5 years old in its current form. Everyone is mid-career-pivot.

Done

• Located your starting bucket.
• Picked the right entry point on this site.

Next: The 12-month arc, honest version →

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