Ordine del packaging del modello

Man mano che entriamo in argomenti più avanzati di MLOps, è importante capire il processo di packaging di un modello di Machine Learning per il deployment in un ambiente di produzione. Questo include una serie di passaggi critici che vanno seguiti in un ordine preciso per garantire che il modello venga distribuito con successo. In questo esercizio metteremo alla prova le tue conoscenze di questi passaggi per vedere se sei pronto ad affrontare le sfide del deployment dei modelli in 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

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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

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!