Designing a Production-Ready Data Science Pipeline: From Raw Events to Monitored Models

A model is only one artifact in a production data science system. The harder engineering problem is creating a reliable path from changing source data to repeatable features, deployable predictions, and measurable outcomes. This article presents a practical architecture for that path. It is intentionally tool-agnostic: cloud products and...