Predicting customer lifetime value

Many businesses take time building models to predict customer lifetime value (CLV) for a subscription-based business. They do this because it helps them forecast demand, revenue, and plenty of other reasons. This model has to be relatively accurate and they want to ensure that the model is not unfairly treating certain groups of customers.

Question: Which of the following testing routines can the team use to check whether the model is treating all customers fairly, regardless of their background or characteristics?

Cet exercice fait partie du cours

Developing Machine Learning Models for Production

Afficher le cours

Exercice interactif pratique

Passez de la théorie à la pratique avec l’un de nos exercices interactifs

Commencer l’exercice

Cet exercice fait partie du cours

Developing Machine Learning Models for Production

IntermédiaireNiveau de compétence

4.8+

Commencer le cours gratuitement

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: Model reliability Exercise 2: Categorizing Testing Strategies for ML Pipeline Readiness and Staleness Exercise 3: Addressing Model Staleness in Machine Learning Pipelines Exercise 4: Testing data Exercise 5: Picking the right test Exercise 6: Consistency check Exercise 7: Testing models Exercise 8: Predicting customer lifetime value

Exercice en cours

Exercise 9: Classifying fairness Exercise 10: Testing machine learning pipelines Exercise 11: Congratulations!