Customer Lifetime Value vorhersagen

Viele Unternehmen investieren Zeit in den Aufbau von Modellen zur Vorhersage des Customer Lifetime Value (CLV) für ein abonnementbasiertes Geschäft. Das tun sie, weil es ihnen hilft, Nachfrage, Umsatz und vieles mehr zu prognostizieren. Dieses Modell muss relativ genau sein, und sie wollen sicherstellen, dass das Modell bestimmte Kundengruppen nicht unfair behandelt.

Frage: Welche der folgenden Test-Routinen kann das Team verwenden, um zu prüfen, ob das Modell alle Kundinnen und Kunden fair behandelt – unabhängig von ihrem Hintergrund oder ihren Merkmalen?

Diese Übung ist Teil des Kurses

<Kurs>ML-Modelle für den Produktionseinsatz entwickeln</Kurs>

Interaktive praktische Übung

Verwandle Theorie mit einer unserer interaktiven Übungen in die Praxis

Diese Übung ist Teil des Kurses

<Kurs>ML-Modelle für den Produktionseinsatz entwickeln</Kurs>

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4.8+

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This chapter will provide you with the skills and knowledge needed to move your machine learning models from the research and development phase into a production environment. You will learn about the process of moving from a research prototype to a reliable, scalable, and maintainable system.

Exercise 1: Adopting an MLOps mindset Exercise 2: What is a key aspect of MLOps?Exercise 3: What is technical debt?Exercise 4: Writing maintainable ML code Exercise 5: Version control Exercise 6: Code organization Exercise 7: Writing effective ML documentation Exercise 8: Why document labeling methods?Exercise 9: MLOps best practices

In this chapter, you’ll learn about the importance of reproducibility in machine learning, and how to ensure that your models remain reproducible and reliable over time. You’ll explore various techniques and best practices that you can use to ensure the reproducibility of your models.

Exercise 1: Designing reproducible experiments Exercise 2: ML reproducibility Exercise 3: Identifying Key Aspects for Reproducibility and Reliability Exercise 4: Sorting Reliability Elements Exercise 5: Sorting Aspects of Reproducibility and Documentation Practices Exercise 6: Feature engineering Exercise 7: Classifying Feature Engineering Goals & Techniques Exercise 8: Identifying Data Manipulation and Transformation Techniques Exercise 9: Feature selection Exercise 10: Data and model versioning Exercise 11: Model store Exercise 12: Versioning training data Exercise 13: Ensuring reproducibility

In Chapter 3, you’ll examine the various challenges associated with deploying machine learning models into production environments. You’ll learn about the various approaches to deploying ML models in production and strategies for monitoring and maintaining ML models in production.

Exercise 1: Packaging ML models Exercise 2: Goal of serialization and containerization Exercise 3: Model packaging order Exercise 4: Scalability Exercise 5: Scaling Exercise 6: Model complexity Exercise 7: Categorizing scaling techniques Exercise 8: Automation Exercise 9: Benefits of continuous delivery Exercise 10: CI/CD steps Exercise 11: Putting ML models into production

In the final chapter, you’ll learn about the various ways to test machine learning pipelines and ensure they perform as expected. You’ll discover the importance of testing ML pipelines and learn techniques for testing and validating ML pipelines.

Exercise 1: Zuverlässigkeit von Modellen Exercise 2: Teststrategien für Einsatzbereitschaft und Staleness von ML-Pipelines kategorisieren Exercise 3: Modellveraltung in Machine-Learning-Pipelines begegnen Exercise 4: Daten testen Exercise 5: Den richtigen Test wählen Exercise 6: Konsistenzprüfung Exercise 7: Modelle testen Exercise 8: Customer Lifetime Value vorhersagen

Aktuelle Übung

Exercise 9: Fairness klassifizieren Exercise 10: ML-Pipelines testen Exercise 11: Glückwunsch!