Where chapters 1 through 10 of this book are about building NLP models, this chapter covers everything that happens outside NLP models. Why is this important? Isn’t NLP all about building high-quality ML models? It may come as a surprise if you don’t have much experience with production NLP systems, but a large portion of an NLP system has very little to do with NLP at all. As shown in figure 11.1, only a tiny fraction of a typical real-world ML system is the ML code, but the “ML code” part is supported by numerous components that provide various functionalities, including data collection, feature extraction, and serving. Let’s use a nuclear power plant as an analogy. In operating a nuclear power plant, only a tiny fraction concerns nuclear reaction. Everything else is a vast and complex infrastructure that supports safe and efficient generation and transportation of materials and electricity—how to use the generated heat to turn the turbine to make electricity, how to cool and circulate water safely, how to transmit the electricity efficiently, and so on. All those supporting infrastructures have little to do with nuclear physics.