The AI Engineering curriculum is coming.
Get Ready. Start with the Fundamentals.
7 foundation modules that prepare you for production AI engineering. Python, object-oriented programming, databases, linear algebra, probability, and APIs. The skills every AI engineer needs. Free and available now.
Why Start Now?
The AI Engineering curriculum covers classical ML, deep learning, natural language processing, and shipping with LLMs. It assumes you can write Python fluently, understand how databases work, and think in vectors and probability. These 94 free lessons get you there.
Think Like an ML Engineer
Not just write code that works, but understand why it works. Memory, performance, tradeoffs.
Understand Where Context Comes From
Every RAG system, every vector search, every prompt starts with data. You will know how to store it, query it, and retrieve it.
Go from Notebook to Production
The engineering skills that turn a prototype into a deployed system. Testing, packaging, containers, continuous integration.
Build What Powers AI Products
The APIs behind every AI product: streaming responses, async pipelines, real-time connections. You will build them, not just consume them.
The Curriculum
Foundations are available now. The AI Engineering track is next.
Foundations
Free6 modules that build the skills you need before touching ML. Each one teaches a capability through a hands-on project.
Python
Understand how Python manages memory, why lists and dicts behave differently, and how to write functions that are easy to test and debug. Not syntax. The mental model.
Software Design
Turn a script into a tested, packaged, containerized application. Classes, Git, pytest, Docker. The workflow real engineering teams use every day.
Data Systems
Know when to use Postgres, when to use Redis, and when vectors are the answer. The data layer behind every AI system, from SQL to pgvector.
Linear Algebra
When someone says "attention is just matrix multiplication," you will know exactly what that means. Vectors, dot products, cosine similarity. The math that powers every model.
Probability & Statistics
Understand how LLMs sample tokens, why Bayes matters for search, and what a distribution actually tells you. The math of uncertainty, made concrete with code.
APIs & Async
Build the services that power AI products. HTTP, async, streaming responses, real-time connections. The exact patterns behind every LLM API you have ever used.
What Comes Next
Coming SoonOnce you have the foundations, the AI Engineering curriculum takes you from classical ML through deep learning to shipping with LLMs.
Classical ML
One investigation: predict which trial accounts convert to paid. 13 lessons, real datasets, MLflow.
Neural Networks & Natural Language Processing
Perceptrons, backprop, PyTorch, word embeddings, transformers.
AI Engineering
Embeddings, RAG, agents, prompt engineering. Ship with LLMs.
Open Source AI
Self-hosting, fine-tuning, working with model weights. Own the full stack.
Interview Prep
System design, coding problems, ML engineering interviews.
How It Works
Sign Up
Create a free account. No credit card required. Start with Python or any foundation module.
Learn by Building
Read the lesson, run the code, do the exercises. Each lesson adds to the module project.
Build the Capstone
After each module, build a project on your own. Markdown organizer, datacheck CLI, A/B test analyzer.
I Built This From What I Learned Shipping AI Systems
Sarah Floris — Lead ML Engineer
There is no shortage of AI content. But most of it is made by people who have never deployed a model, never debugged inference at scale, never built a pipeline that runs in production. I have shipped AI systems processing 8.6M predictions per day, saving teams 1,080 hours per week, and forecasting on $3B in assets. This curriculum is different because it is built from that experience.
The AI Engineering curriculum is coming. Be ready.
94 foundation lessons across 7 modules. All free. Start now so you hit the ground running when the full curriculum drops.
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