Learning Machine Learning After Years of Backend Engineering
A few habits from systems work that have helped and a few that have needed unlearning.
Learning machine learning after years of backend work has been pleasantly familiar in some ways and disorienting in others.
The familiar part is the instinct to make a model of the system before changing it: what goes in, what comes out, where the uncertainty lives, and which measurements matter. The disorienting part is that a perfectly valid program can still produce a deeply unhelpful model. Correctness is not the whole story.
For now, I am treating this as a notebook for the questions that turn out to matter more than the first answers.
A useful starting point
In backend systems, I often begin by looking for the boundary conditions. In ML, the boundary is frequently the data: what it represents, what it leaves out, and whether it resembles the world in which a model will be used.