Production-Level-Deep-Learning

Why we included this project

Most machine learning guides stop once the model trains, but this one focuses on everything that happens after: scoping a project, managing the lifecycle, designing the supporting infrastructure, and shipping systems that hold up with real users. It's a curated reading list that gathers the Full Stack Deep Learning bootcamp, the TFX workshop, and KubeFlow meetup material into one organized place. Engineers and tech leads who want a map of what production ML actually requires before committing to a stack will find it useful. It isn't deployable software, but as a reference and study guide it gives teams a practical starting point for planning their own pipelines and infrastructure.