Production brief
Users, data, constraints, metric, latency budget, and the cost of being wrong.
The production-first MLE courseLearn the model.
Learn the model.
Engineer the system.
A rigorous path from k-nearest neighbors to distributed transformers—built around the failures, constraints, and decisions that define real ML engineering.
- 20
- production courses
- 105
- guided lessons
- 24
- industry cases
- 0
- black boxes
LIVE ROADMAP
MODELSYSTEMSCALE
kNNXGBoostBackpropGPU / DDPAttentionProduction
Path ready~300 h
PROBLEM FIRSTFORMULAS EXPLAINEDSYSTEMS INCLUDEDNO ACCOUNTSPROGRESS STAYS LOCAL
Continue learning · stored locally
Choose a starting point
Take the six-question diagnostic or begin with reproducible engineering foundations.
One connected field
ML engineering is not a stack of disconnected tutorials.
Follow the dependency spine, switch into a role track, or jump directly to the concept blocking your current system.
01
Foundations
02
Classical ML
03
Deep learning
04
Training systems
05
Transformers
06
Production
All nodes stay unlocked. Prerequisites explain the fastest path; they never become gates.
Representative lesson · EvaluationThe model is accurate.
The model is accurate.
The decision is expensive.
A fraud classifier returns probabilities—not business actions. Move the threshold and watch recall, precision, and cost pull in different directions.
Expected decision cost
C(t) = cFP · FP(t) + cFN · FN(t)
- t
- decision threshold
- c
- business cost per error
Exact computation · 20 observations
Tune the decision threshold
False negatives cost 18 units. False positives cost 4. Find the operating point, not just the model.
Precision56%
Recall63%
Business cost70
View the nonvisual result table
| True positive | False positive | True negative | False negative |
|---|---|---|---|
| 5 | 4 | 8 | 3 |
The chapter contract
Every chapter begins with stakes and ends with an operating system.
Model the idea
Intuition, derivation, shape tables, numeric examples, and equation-to-code mapping.
Stress the system
Deterministic labs expose failure modes, capacity limits, drift, and tradeoffs.
Ship the resolution
Architecture, observability, rollout, rubric, and a postmortem you can reuse.
Production casebook · 01
When one feature pipeline must serve thousands of models.
Reconstruct Uber's Michelangelo platform: online/offline consistency, model registry, outcome joins, serving paths, and the ownership boundaries that keep it operable.
Open the case reconstruction ↗