Builder
Machine Learning Engineer
Train, measure, and ship models. In 2026 you also know when an LLM is the wrong tool.
2–3 weeks · train, measure, know when not to use an LLM
0/11 lessons on this path
US
$120K–$250K+
Canada
CA$100K–$160K+
India
₹15–40 LPA+
What this path is (and is not)
Enough to explain train vs RAG vs fine-tune with a small model in hand. Mastery is production drift over months.
Mastery syllabus
1 · Data to guess
A picture of training you can teach a child, then a graduate.
2 · When not an LLM
Fine-tune changes behaviour. RAG changes facts.
3 · Holdout and baseline
Split first. Boring model first. Then ask if an LLM is rent-worthy.
4 · Drift you can say
March is not August. Fashion is not a metric.
Simulation labs
Holdout thinking
A metric that can fail.
Open the labFeatures as a map
Nearby is similar, not proven.
Open the labHoldout box
Score only the sealed set.
Open the labBoring first
Beat the small model before the LLM.
Open the lab
The project that hires
A small model with a holdout metric and a note on when an LLM is worse
- One metric. One holdout.
- A drift sentence: what would you watch next month.
- A paragraph: why an LLM would have been the wrong tool.
Interview room
- Walk through train, holdout, and a drift alert.
- RAG vs fine-tune vs train-from-scratch for a fraud flag. Pick one and defend it.
- You liked the train score. Why is that not enough?
- Write the drift sentence for next month.
Resume lines that hire
- Shipped a classifier with a holdout metric and a drift alert — not a notebook demo.