# Production ML systems

> [!NOTE]
> **Estimated module length:** 70 minutes

> [!NOTE]
> **Learning objectives**
>
> - Appreciate the breadth of components in a production ML system.
> - Pick the ideal training paradigm (static versus dynamic).
> - Pick the ideal inference paradigm (static versus dynamic).
> - Test your machine learning deployment.
> - Ask the right questions about your production ML system.
> - Determine flaws in real-world ML models.
> - Monitor the components in a production ML system.

> [!CAUTION]
> **Prerequisites:**
>
> This module assumes you are familiar with the concepts covered in the
> following modules:
>
> - [Introduction to Machine Learning](https://developers.google.com/machine-learning/intro-to-ml)
> - [Linear regression](https://developers.google.com/machine-learning/crash-course/linear-regression)
> - [Working with numerical data](https://developers.google.com/machine-learning/crash-course/numerical-data)
> - [Working with categorical data](https://developers.google.com/machine-learning/crash-course/categorical-data)
> - [Datasets, generalization, and overfitting](https://developers.google.com/machine-learning/crash-course/overfitting)

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.com/static/machine-learning/crash-course/images/MlSystem.png) **Figure 1.** A real-world production ML system comprises many components.

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.
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