April 2026
Intermediate
586 pages
17h 40m
French
Content preview from Apprendre AutoML (French Edition)
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Partie IV. Production et MLOps
Construire des modèles précis, c'est seulement la moitié du chemin. L'autre moitié — souvent la plusdifficile —consiste à mettre ces modèles en production, à les faire fonctionner de manière fiable et à s'assurer qu'ils continuent à être performants alors que le monde change autour d'eux. C'est là que de nombreux projets AutoML échouent — non pas à cause de la qualité des modèles, mais à cause de lacunes opérationnelles.
Dans cette partie, on comble le fossé entre l'AutoML et MLOps. Tu apprendras à intégrer l'AutoML dans des plateformes ML modernes comme MLflow, à construire des pipelines de données fiables avec Apache Airflow et à mettre en œuvre des pratiques CI/CD qui traitent le déploiement des modèles avec la même rigueur que le déploiement de logiciels. Ce ne sont pas des bonnes pratiques théoriques, mais des approches éprouvées issues de systèmes de production traitant des millions de prédictions chaque jour. Cette partie se termine par les modèles opérationnels nécessaires pour déployer et maintenir l'AutoML en production.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.