Production ML systems

AI-generated Key Takeaways

• This module explores the broader ecosystem of a production ML system, emphasizing that the model itself is only a small part of the overall system.

• You will learn to choose the appropriate training and inference paradigms (static or dynamic) based on your specific needs.

• The module covers key aspects of production ML systems, including testing, identifying potential flaws, and monitoring the system's components.

• As a prerequisite, familiarity with foundational machine learning concepts, including linear regression, data types, and overfitting, is assumed.

• Building upon previous modules, this content shifts focus to the practical aspects of deploying and maintaining ML models in real-world scenarios.

Estimated module length: 70 minutes

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.

Prerequisites:

This module assumes you are familiar with the concepts covered in the following modules:

• Introduction to Machine Learning
• Linear regression
• Working with numerical data
• Working with categorical data
• Datasets, generalization, and 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. 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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