From Pipeline to Model: A Data Engineer's Production ML Playbook

As data engineering pipelines increasingly demand machine learning components, engineers without specialized ML backgrounds must learn to ship models in production. This article distills real patterns from seven production projects, proving that strong results don't require expensive cloud platforms or proprietary APIs—just the right open-source tools and battle-tested approaches. For any data engineer facing that critical question when data alone cannot answer, these practical patterns are directly applicable and production-tested.