ML Engineering Foundations
For developers moving from app code into machine learning.
The core math and primitives behind modern ML, made runnable: feel how models learn, implement the functions every model relies on, then assemble them into a recommender and a retrieval pipeline.
- Build intuition for how gradient descent trains a model
- Implement softmax, cosine similarity and top-k ranking from scratch
- Connect the primitives to a real recommendation system
- See where retrieval fits in a modern ML stack
Gradient Descent: The Descent
Play with how a model actually learns.
Softmax
The function behind every classifier head.
Cosine Similarity
How embeddings are compared.
Top-K Retrieval
Rank candidates by similarity.
Attention Is All You Need
See those exact primitives — similarity + softmax — assembled into the Transformer.
Design a Recommendation System
Assemble the primitives into a recommender.
Design a RAG Pipeline
Where retrieval fits in a modern stack.
From app code into ML
You don't need a research background to build with machine learning, but a handful of core ideas repay understanding directly — and this path makes them runnable. Feel how a model learns by descending a gradient, implement the softmax every model head relies on, then the similarity and ranking that power search and recommendations.
Each concept is a lab you drive or a function you implement with tests, not a formula to memorise. By the end the rest of AI engineering — a recommender, a retrieval pipeline, an embedding search — reads as combinations of primitives you've built yourself.