Classer les aspects de la reproductibilité et les pratiques de documentation

Dans cet exercice, vous allez explorer l’importance de la reproductibilité en Machine Learning et le rôle de la documentation pour garantir des résultats fiables. Nous nous concentrerons sur deux catégories : les aspects de la reproductibilité et les pratiques de documentation. Voyons si vous pouvez classer chaque élément dans le bon panier !

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<cours>Développer des modèles de Machine Learning pour la production</cours>

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Cet exercice fait partie du cours

<cours>Développer des modèles de Machine Learning pour la production</cours>

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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: Concevoir des expériences reproductibles Exercise 2: Reproductibilité en ML Exercise 3: Identifier les éléments clés pour la reproductibilité et la fiabilité Exercise 4: Classer les éléments de fiabilité Exercise 5: Classer les aspects de la reproductibilité et les pratiques de documentation

Exercice actuel

Exercise 6: Feature engineering Exercise 7: Classer objectifs et techniques d’ingénierie des features Exercise 8: Identifier les techniques de manipulation et de transformation des données Exercise 9: Sélection de variables Exercise 10: Versionnage des données et des modèles Exercise 11: Model store Exercise 12: Versionnage des données d’entraînement Exercise 13: Garantir la reproductibilité

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