Modelveroudering aanpakken in Machine Learning-pijplijnen

Modelbetrouwbaarheid in machine learning draait niet alleen om prestaties, maar ook om factoren zoals data en omgeving, latentie en de snelheid van het model. Drie belangrijke soorten tests voor modelbetrouwbaarheid zijn unittests, integratietests en smoketests. Modelveroudering treedt op wanneer de prestaties van een model in de loop van de tijd afnemen door veranderingen in de data of de omgeving.

Vraag: Welke van de volgende acties kunnen helpen om modelveroudering aan te pakken? (Selecteer alle toepasselijke opties)

Deze oefening maakt deel uit van de cursus

Machine Learning-modellen ontwikkelen voor productie

Cursus bekijken

Praktische interactieve oefening

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

Begin met trainen

Deze oefening maakt deel uit van de cursus

Machine Learning-modellen ontwikkelen voor productie

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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: 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: Betrouwbaarheid van modellen Exercise 2: Teststrategieën categoriseren voor ML-pipelinegereedheid en -veroudering Exercise 3: Modelveroudering aanpakken in Machine Learning-pijplijnen

Huidige oefening

Exercise 4: Gegevens testen Exercise 5: De juiste test kiezen Exercise 6: Consistentiecontrole Exercise 7: Modellen testen Exercise 8: Klantlevenswaarde voorspellen Exercise 9: Gelijkheid classificeren Exercise 10: ML-pipelines testen Exercise 11: Gefeliciteerd!