This repository serves as a practical guide for deploying deep learning models at scale, covering the full ML project lifecycle from development to production. It addresses common failure points in AI projects and provides architecture guidance for production ML systems. The content draws from established sources including the Full Stack Deep Learning Bootcamp and covers tooling like Kubeflow and TensorFlow Extended (TFX) for pipeline development.