Garantire la riproducibilità

Sai come garantire la riproducibilità nel tuo ciclo di vita MLOps in diversi modi. Puoi distribuire servizi per archiviare, mantenere e monitorare le feature. Puoi progettare esperimenti di machine learning efficaci e manipolare le feature per massimizzare l’efficienza del modello e ridurre la latenza in produzione.

Per essere davvero efficace, dovrai ricordare le migliori pratiche di riproducibilità. È il momento di mettere alla prova queste conoscenze!

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Sviluppare modelli di Machine Learning per la produzione

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Sviluppare modelli di Machine Learning per la produzione

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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: Progettare esperimenti riproducibili Exercise 2: Riproducibilità in ML Exercise 3: Individuare gli aspetti chiave per riproducibilità e affidabilità Exercise 4: Ordinare gli elementi di affidabilità Exercise 5: Ordinare aspetti della riproducibilità e pratiche di documentazione Exercise 6: Feature engineering Exercise 7: Classificare obiettivi e tecniche di Feature Engineering Exercise 8: Individuare tecniche di manipolazione e trasformazione dei dati Exercise 9: Selezione delle feature Exercise 10: Versioning di dati e modelli Exercise 11: Model store Exercise 12: Versionare i dati di training Exercise 13: Garantire la riproducibilità

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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!