Why MLOps
Running a model is harder than building one. Start with what Geo-MLOps solves.
The first model of a machine-learning project is usually born on one person's laptop. The trouble starts afterwards. As models multiply, data changes and field devices number in the dozens, nobody can quickly answer: "Which model is running in the field right now — trained on what data, by whom, and when?"
MLOps does for models what DevOps does for software: it turns building models repeatedly, shipping them safely and watching them continuously into a system. Geo-MLOps delivers that system as a platform you can install and use right away.
In this chapter
| Page | Time | What it covers |
|---|---|---|
| Problems in production | 3 min | Five reasons running models is hard, and how the platform responds |
| What Geo-MLOps does | 3 min | The feature map — menus and what they do |
| Before and after | 3 min | The same job done by hand and with the platform |
| Core concepts | 5 min | Tenants, datasets, experiments, model versions, approval, serving, edge, drift |
| Roles and permissions | 3 min | Who can do what |