Most machine learning projects stall in the same place: a model that works in a notebook and nowhere else. The gap is rarely the model itself — it is everything around it.
Why models stall before production
A notebook captures a result, not a system. The data pipeline behind it often exists only in one person's shell history, and the environment that produced the result is rarely reproducible a month later.
- Training data assembled by hand and never versioned
- No evaluation beyond a single held-out split
- No owner once the original author moves on
What production actually requires
Getting to production means treating the model as one component of a running system, with the same expectations as any other service.
Reproducible pipelines
Every training run should be repeatable from a commit and a data version. If you cannot rebuild last month's model exactly, you cannot debug it.
Monitoring that watches the data
Standard software breaks when the code changes. ML systems also break when the world changes and the input distribution moves underneath a model that has not been retrained.
Where to start
Pick the narrowest slice that delivers value, put it behind a real interface, and instrument it before scaling scope.
