ML-modellen in productie brengen

Je kent verschillende best practices voor het in productie brengen van modellen in je MLOps-levenscyclus. Je kunt automatisering inzetten om modellen sneller bij je gebruikers te krijgen. Je kunt de schaalbaarheid en efficiëntie van modellen maximaliseren door omgevingen en modellen effectief te verpakken.

Om echt effectief te zijn, moet je deze best practices paraat hebben en weten hoe ze je ML-pipeline precies helpen. Tijd om je kennis te testen!

Deze oefening maakt deel uit van de cursus

Machine Learning-modellen ontwikkelen voor productie

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Deze oefening maakt deel uit van de cursus

Machine Learning-modellen ontwikkelen voor productie

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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: ML-modellen verpakken Exercise 2: Doel van serialiseren en containerizen Exercise 3: Volgorde voor modelpackaging Exercise 4: Schaalbaarheid Exercise 5: Schaalbaarheid Exercise 6: Modelcomplexiteit Exercise 7: Schaaltechnieken categoriseren Exercise 8: Automatisering Exercise 9: Voordelen van continuous delivery Exercise 10: CI/CD-stappen Exercise 11: ML-modellen in productie brengen

Huidige oefening

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!