MLOps for Small Teams: The Minimum Reliable Stack for Weekly Releases

Teams with fewer than ten engineers can still ship robust machine learning systems by standardizing a compact deployment stack. The essentials are straightforward: reproducible training jobs, versioned data contracts, online-offline feature parity checks, and clear rollback paths.

Reliability issues often come from hidden coupling between feature generation and serving environments. A shared feature specification and pre-deploy validation suite can eliminate most regressions before they hit production.

The best MLOps strategy is not maximum tooling; it is minimum operational ambiguity.