Mettere i modelli di ML in produzione

Conosci diverse best practice quando si tratta di mettere i modelli in produzione nel tuo ciclo di vita MLOps. Puoi usare strumenti di automazione per consegnare i modelli agli utenti più velocemente. Puoi massimizzare la scalabilità e l'efficienza dei modelli effettuando un packaging efficace di ambienti e modelli.

Per essere davvero efficace, dovrai ricordare queste best practice e sapere come aiutano nello specifico la tua pipeline di ML. 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: 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: Impacchettare i modelli di ML Exercise 2: Obiettivo di serializzazione e containerizzazione Exercise 3: Ordine del packaging del modello Exercise 4: Scalabilità Exercise 5: Scalabilità Exercise 6: Complessità del modello Exercise 7: Classificare le tecniche di scaling Exercise 8: Automazione Exercise 9: Vantaggi della Continuous Delivery Exercise 10: Passaggi CI/CD Exercise 11: Mettere i modelli di ML in produzione

Esercizio in corso

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!