Unit economics, build versus buy, and making the case for an ML investment to people who do not build models.
6h · 6 lessons · 1 challenge
Before this: Monitoring, Drift & Reliability
Senior ML engineers are asked what something will cost and whether it is worth it. This module builds that fluency: cost per prediction, cost per training run, the hosted-versus-self-hosted arithmetic, and the capacity planning behind a launch.
Ends with writing the one-page case for an ML investment — the artefact that actually gets projects approved.
A production system and its bill. Halve the spend without dropping below the quality floor or breaking the latency SLA.
Worth 500 XP.
