AI Systems: From Theory to Production
AI Systems: From Theory to Production Research teaches us what is possible, but production shows what is reliable. In the real world, AI models must not only be accurate; they must run fast, stay trustworthy, and be easy to maintain. The shift from theory to production requires careful planning, good data, and steady monitoring. This article shares practical ideas to bridge the gap. A well designed AI system starts with business goals and clear metrics. It then moves through data quality, feature engineering, model choice, and rigorous testing. After deployment, the work shifts to observability, governance, and ongoing improvement. Each stage depends on the others, so teams that plan end-to-end tend to succeed. ...