Mit Sammlungen den Überblick behalten
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Bisher haben wir uns in diesem Kurs auf die Erstellung von Modellen für maschinelles Lernen (ML) konzentriert.
Wie Abbildung 1 jedoch zeigt, sind reale Produktions-ML-Systeme große Ökosysteme und das Modell ist nur ein einzelner, relativ kleiner Teil.
Abbildung 1: Ein reales ML-System für die Produktion besteht aus vielen Komponenten.
Im Mittelpunkt eines realen Produktionssystems für maschinelles Lernen steht das ML
Modellcode, stellt aber häufig nur 5% oder weniger der gesamten Codebasis in
System. Das ist kein Fehldruck, ist es deutlich weniger, als Sie
zu erwarten ist. Beachten Sie, dass ein ML-Produktionssystem erhebliche Ressourcen
den Eingabedaten hinzu: Sie sammeln,
verifizieren und Funktionen daraus extrahieren.
[null,null,["Zuletzt aktualisiert: 2025-07-27 (UTC)."],[[["\u003cp\u003eThis module explores the broader ecosystem of a production ML system, emphasizing that the model itself is only a small part of the overall system.\u003c/p\u003e\n"],["\u003cp\u003eYou will learn to choose the appropriate training and inference paradigms (static or dynamic) based on your specific needs.\u003c/p\u003e\n"],["\u003cp\u003eThe module covers key aspects of production ML systems, including testing, identifying potential flaws, and monitoring the system's components.\u003c/p\u003e\n"],["\u003cp\u003eAs a prerequisite, familiarity with foundational machine learning concepts, including linear regression, data types, and overfitting, is assumed.\u003c/p\u003e\n"],["\u003cp\u003eBuilding upon previous modules, this content shifts focus to the practical aspects of deploying and maintaining ML models in real-world scenarios.\u003c/p\u003e\n"]]],[],null,["# Production ML systems\n\n| **Estimated module length:** 70 minutes\n| **Learning objectives**\n|\n| - Appreciate the breadth of components in a production ML system.\n| - Pick the ideal training paradigm (static versus dynamic).\n| - Pick the ideal inference paradigm (static versus dynamic).\n| - Test your machine learning deployment.\n| - Ask the right questions about your production ML system.\n| - Determine flaws in real-world ML models.\n| - Monitor the components in a production ML system.\n| **Prerequisites:**\n|\n| This module assumes you are familiar with the concepts covered in the\n| following modules:\n|\n| - [Introduction to Machine Learning](/machine-learning/intro-to-ml)\n| - [Linear regression](/machine-learning/crash-course/linear-regression)\n| - [Working with numerical data](/machine-learning/crash-course/numerical-data)\n| - [Working with categorical data](/machine-learning/crash-course/categorical-data)\n| - [Datasets, generalization, and overfitting](/machine-learning/crash-course/overfitting)\n\nSo far, this course has focused on building machine learning (ML) models.\nHowever, as Figure 1 suggests, real-world production ML systems are large\necosystems and the model is just a single, relatively small part.\n**Figure 1.** A real-world production ML system comprises many components.\n\nAt the heart of a real-world machine learning production system is the ML\nmodel code, but it often represents only 5% or less of the total codebase in\nthe system. That's not a misprint; it's significantly less than you might\nexpect. Notice that an ML production system devotes considerable resources\nto the input data: collecting it, verifying it, and extracting features from it. \n[Help Center](https://support.google.com/machinelearningeducation)"]]
