Approche centrée données vs centrée modèle

Comme vous venez de l’entendre, il existe deux grandes approches pour améliorer les performances d’un modèle de ML :

L’approche centrée modèle, et
L’approche centrée données

Pouvez-vous dire laquelle des actions suivantes correspond à chacune de ces deux philosophies ?

Cet exercice fait partie du cours

<cours>Déploiement MLOps et cycle de vie</cours>

Exercice interactif pratique

Transformez la théorie en action avec l’un de nos exercices interactifs

Commencer l’exercice

Cet exercice fait partie du cours

<cours>Déploiement MLOps et cycle de vie</cours>

AvancéNiveau de compétence

4.8+

Commencer le cours gratuitement

This chapter gives a high-level overview of MLOps principles and framework components important for deployment and life cycling.

Exercise 1: The modern MLOps framework Exercise 2: ML workflows Exercise 3: MLOps benefits Exercise 4: Life-cycling stages Exercise 5: App vs. model Exercise 6: Decommissioning Exercise 7: The model life cycle: recap Exercise 8: MLOps components Exercise 9: Automated sequence Exercise 10: Stores and registries Exercise 11: DevOps or MLOps?

This chapter is dedicated to all the considerations we need to make already in the development phase, in order to ensure a smooth ride when we reach the operations. Our ultimate goal is to explain how to train the model using MLOps best practices and build a model package that enables smooth deployment, reproducibility and post-deployment monitoring.

Exercise 1: Deployment-driven development Exercise 2: Testing your machine learning model Exercise 3: Best time to start deploying Exercise 4: Profiling, versioning, and feature stores Exercise 5: Feature store properties Exercise 6: Profiling and feature store benefits Exercise 7: Ensuring reproducibility Exercise 8: Model build pipelines in CI/CD Exercise 9: Different pipelines Exercise 10: Model build pipeline properties Exercise 11: CI/CD integration Exercise 12: Model packaging Exercise 13: Model formats Exercise 14: Full package

This chapter deals with critical model operations questions such as: - What are the different ways in which we can serve our models? - What is an API, and what are its key functionalities? - How do we thoroughly test our service before making it available to the end users? - How do we update models in production without service disturbance? You will learn about batch prediction, real-time prediction, input and output data validation, unit testing, integration testing, canary deployment, and much more.

Exercise 1: Serving modes Exercise 2: Offline or online?Exercise 3: When time matters - a bit Exercise 4: Building the API Exercise 5: Client-server Exercise 6: API functionalities Exercise 7: Deployment progression and testing Exercise 8: Which test is it?Exercise 9: Progression through environments Exercise 10: Tests per environment Exercise 11: Model deployment strategies Exercise 12: A fitting deployment strategy Exercise 13: Order of risk Exercise 14: Shadow of the shadow

This final chapter is dedicated to monitoring and maintaining ML services after they are deployed, as well as to model governance. You will cover crucial concepts such as verification latency, covariate shift, concept drift, human-in-the-loop systems, and more.

Exercise 1: Surveiller les services de Machine Learning Exercise 2: Shift vs drift Exercise 3: Latence Exercise 4: Déjà ?Exercise 5: Suivi et alertes Exercise 6: Le système de monitoring Exercise 7: Alerte Exercise 8: Maintenance du modèle Exercise 9: Approche centrée données vs centrée modèle

Exercice actuel

Exercise 10: Humain dans la boucle Exercise 11: Gouvernance des modèles Exercise 12: Éléments de gouvernance Exercise 13: Étapes de la gouvernance Exercise 14: Classification des risques Exercise 15: Conclusion