So far, this course has focused on building machine learning (ML) models. However, as Figure 1 suggests, real-world production ML systems are large ecosystems and the model is just a single, relatively small part.
![Figure 1. ML system diagram containing the following components:
data collection, feature extraction, process management tools,
data verification, configuration, machine resource management,
monitoring, serving infrastructure, and ML model code. The ML
model code part of the diagram is dwarfed by the other nine
components.](https://developers.google.cn/static/machine-learning/crash-course/images/MlSystem.png?authuser=3)
At the heart of a real-world machine learning production system is the ML model code, but it often represents only 5% or less of the total codebase in the system. That's not a misprint; it's significantly less than you might expect. Notice that an ML production system devotes considerable resources to the input data: collecting it, verifying it, and extracting features from it.