MLOps-best practices

Je hebt goed inzicht in manieren om technische schuld in je Machine Learning-pijplijnen te verminderen door effectieve documentatie te schrijven en onderhoudbare code te maken. Het lastige is weten wanneer je een specifieke best practice gebruikt en genoeg best practices toepassen om de vele manieren van het terugdringen van technische schuld te dekken, waaronder onderhoudbare code en goede documentatie.

In deze oefening categoriseer je elke best practice in de bucket waar die het beste thuishoort, zodat je weet of je bij het toepassen van deze praktijken elk hoofdgebied met minimaal één best practice afdekt. Tijd om je kennis te testen!

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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: Een MLOps-mentaliteit aannemen Exercise 2: Wat is een belangrijk aspect van MLOps?Exercise 3: Wat is technische schuld?Exercise 4: Onderhoudbare ML-code schrijven Exercise 5: Versiebeheer Exercise 6: Code-organisatie Exercise 7: Effectieve ML-documentatie schrijven Exercise 8: Waarom documenteer je labelmethoden?Exercise 9: MLOps-best practices

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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: 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!