August 2026
Intermediate
548 pages
18h 47m
French
Content preview from Ingénierie des données pour l'IA multimodale (French Edition)
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Chapitre 8. Utilisation du MLOps pour les applications d'IA multimodale
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Quand tu penses au ML traditionnel, tu imagines sans doute des modèles qui fonctionnent principalement avec un seul type de données ou des données structurées. L’IA multimodale repose sur le ML ; c’est le ML qui fournit les algorithmes et les techniques permettant aux systèmes d’IA d’apprendre à partir de différents types de données et de les traiter. Cependant, l’IA multimodale va plus loin en exploitant des architectures ML plus avancées, comme Deep Learning, pour gérer la complexité liée à l’intégration de sources de données variées.
Dans ce chapitre, on va explorer les cadres opérationnels, les outils et les pratiques indispensables à la gestion des systèmes d’IA multimodale en production. Compte tenu de la complexité supplémentaire des pipelines de données multimodales (qui transportent du texte, des images, de l’audio, de la vidéo et des données de capteurs), le MLOps doit prendre en charge l’orchestration des données, le suivi des expériences, les workflows CI/CD, le réentraînement automatisé et la collaboration entre les équipes. Pour ça, le MLOps combine le développement de l’apprentissage automatique (ML) avec les opérations (Ops) pour garantir que les systèmes d’IA soient évolutifs, fiables et optimisés en continu.
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