The AI Engineering Roadmap
Beginner-focused, with full resources.
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The AI Engineering Roadmap
We just put together this AI Engineering roadmap for beginners who want to start today:
Here are the resources:
1️⃣ Master Python
While many are busy vibe coding, those with strong coding fundamentals will always stand out.
Python is the language AI community speaks, and Harvard's CS50p is the best place to learn it.
2️⃣ AI with Python
Once you're done with the fundamentals, it's the right time to understand how Python is used in AI.
This 4-hour course by Andrew Ng is a great starting point.
3️⃣ Maths for ML
Whenever you feel stuck and math becomes a hurdle, these YouTube playlists by Khan Academy are a goldmine.
No need to finish them in one go, watch them over the course of your journey.
4️⃣ Understanding LLMs
These four videos by 3Blue1Brown are arguably the best visual explainers of LLMs and their internal workings.
How LLMs work
Transformers Deep-dive
Attention in transformers
How LLMs store facts
5️⃣ LLM research
Now that you understand what LLMs are, it's time to learn how to build them yourself.
This is the greatest series by the greatest teacher in the world.
Neural nets zero-to-hero by Andrej Karpathy
6️⃣ AI Agents
Before jumping into AI agents, everyone should read Anthropic AI's guide on building effective agents.
"You don't need complex frameworks or libraries, but rather composable patterns"
Check this guide by Anthropic →
7️⃣ Applied AI
We don't recommend chasing frameworks, but we took this course on CrewAI when we started. It's clear, practical, and teaches you to think of agents like humans working together
8️⃣ AI Protocols (MCP)
Now that you understand what agents are, it's time to connect them to tools, APIs, and databases.
We published this free hands-on guide on MCP with 10+ projects (40,000 + downloads)
9️⃣ Project-based learning
This GitHub repo contains 75+ projects on AI Engineering.
Everything is 100% open-source!
🔟 Books
To summarize, here's what we covered:
Programming (Python)
Maths
LLM fundamentals
Building LLMs/ LLM research
AI agents and applied AI
AI protocols
AI engineering projects
Book(s)
Never chase frameworks since they come and go. Master the fundamentals.
Thanks for reading!
P.S. For those wanting to develop “Industry ML” expertise:
At the end of the day, all businesses care about impact. That’s it!
Can you reduce costs?
Drive revenue?
Can you scale ML models?
Predict trends before they happen?
We have discussed several other topics (with implementations) that align with such topics.
Here are some of them:
Learn everything about MCPs in this crash course with 9 parts →
Learn how to build Agentic systems in a crash course with 14 parts.
Learn how to build real-world RAG apps and evaluate and scale them in this crash course.
Learn sophisticated graph architectures and how to train them on graph data.
So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.
Learn how to run large models on small devices using Quantization techniques.
Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using Conformal Predictions.
Learn how to identify causal relationships and answer business questions using causal inference in this crash course.
Learn how to scale and implement ML model training in this practical guide.
Learn techniques to reliably test new models in production.
Learn how to build privacy-first ML systems using Federated Learning.
Learn 6 techniques with implementation to compress ML models.
All these resources will help you cultivate key skills that businesses and companies care about the most.