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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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-10-09 UTC."],[[["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."]]],[]]