This talk presents an opinionated, vendor-neutral blueprint for delivering ML to production reliably. I will cover the minimum viable pipeline: data contracts and quality checks, feature definitions with ownership, experiment tracking and reproducibility, promotion gates tied to evals, safe rollout patterns (blue/green, shadow), and lightweight monitoring for drift, quality, and cost.

You’ll get a reference lifecycle, a promotion checklist, and anti-patterns to avoid (pipeline sprawl, silent schema breaks, unmanaged “notebook ops”). Walk away with a practical template you can apply on any stack.