Reproduceerbaarheid waarborgen

Je weet op meerdere manieren hoe je reproduceerbaarheid in je MLOps-levenscyclus waarborgt. Je kunt services uitrollen om features op te slaan, te onderhouden en te rapporteren. Je kunt effectieve Machine Learning-experimenten ontwerpen en je Machine Learning-features manipuleren om de model efficiëntie te maximaliseren en de latentie in productie te verlagen.

Om echt effectief te zijn, moet je de beste praktijken voor reproduceerbaarheid kunnen reproduceren. Tijd om je kennis te testen!

Deze oefening maakt deel uit van de cursus

Machine Learning-modellen ontwikkelen voor productie

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Zet theorie om in actie met een van onze interactieve oefeningen.

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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: Reproduceerbare experimenten ontwerpen Exercise 2: Reproduceerbaarheid in ML Exercise 3: Belangrijke aspecten voor reproduceerbaarheid en betrouwbaarheid herkennen Exercise 4: Betrouwbaarheidselementen sorteren Exercise 5: Aspecten van reproduceerbaarheid en documentatiepraktijken sorteren Exercise 6: Feature engineering Exercise 7: Doelen en technieken van feature engineering classificeren Exercise 8: Technieken voor gegevensmanipulatie en -transformatie herkennen Exercise 9: Feature selection Exercise 10: Versiebeheer van data en modellen Exercise 11: Model store Exercise 12: Versiebeheer van trainingsdata Exercise 13: Reproduceerbaarheid waarborgen

Huidige oefening

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!