This roadmap is written with absolute beginners in mind but goes beyond surface-level concepts. Every step has been reviewed by an industry veteran, so what you are learning reflects how AI engineering is practiced in the real world.
Our sincere hope is that this roadmap gives you the direction you seek
The misconception we commonly see is in authors describing an ML engineer as an AI engineer, which often leads to the wrong roadmap. Let’s clarify the difference.
AI engineering builds on software engineering. If you’re new, start here:
- Learn a programming language (Python, including creating and managing Python virtual environments)
- Understand Git & version control
- Learn about APIs and how services communicate
- Learn deployment with containers
- The fundamentals of working in the command line
If you already code, you can jump straight into the roadmap below.
This phase helps you build the right foundation.
Understand the big picture and the key concepts that define the field.
Your core tool for building AI systems.
Paid alternatives
- Introduction to Computer Science and Programming - edX MITx
- Introduction to Computational Thinking and Data Science - edX MITx
Focus on Linear Algebra, Calculus, Probability, and Statistics.
- Essence of Linear Algebra – 3Blue1Brown
- Essence of Calculus – 3Blue1Brown
- Probability Explained – Khan Academy
- Statistics Fundamentals – Khan Academy
At this stage, you start connecting the dots.
No need going deep here.
Understand how these models learn from data and build your first neural network from scratch.
Explore how modern systems like GPT and Claude are built.
Theory is essential, but real understanding comes from building.
Connect to powerful pre-trained models using services like OpenAI.
Mastering how to communicate with models determines the quality of your results.
Build simple apps on top of LLMs. Connect LLMs to your own data using vector databases and embeddings.
- Build a Retrieval Augmented Generation (RAG) App - LangChain
- Short-term memory - LangChain
- Long-term memory - LangChain
AI agents are where things get exciting.
With a strong foundation and some hands-on projects under your belt, it’s only natural to start pushing further towards professional AI engineering.
Breaking into a new field, or just learning something new, can be humbling at first. I’ve been there before, but I often remind myself that if I just keep going, I’ll eventually cross that threshold where things finally start to fall into place. James Clear calls this threshold the Plateau of Latent Potential; that tipping point where all the unseen effort finally turns into visible progress. Just start and keep going!
We will truly appreciate it if you:
- Follow Guidely on Linkedin and/or X to stay updated on our upcoming guides, resources, and more.
- Star this repo
Note: A detailed version of this roadmap is available here.
We welcome contributions. Just fork and open a PR.