March 2025
Intermediate to advanced
474 pages
15h 29m
French
book
Systèmes de production à apprentissage automatique
by Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu
Content preview from Systèmes de production à apprentissage automatique
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Chapitre 13. Modélisation de l'infrastructure de service
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Comme n'importe quelle autre application, ton infrastructure ML peut être formée et déployée sur place, sur ta propre infrastructure matérielle. Cependant, cette approche nécessite l'acquisition du matériel (machines physiques) et des GPU pour la formation et l'inférence de grands modèles (réseaux neuronaux profonds, ou DNN). Cette solution peut être viable pour les grandes entreprises qui exécutent et maintiennent des applications ML pendant longtemps.
L'option viable pour les petites et moyennes entreprises et les équipes individuelles est de se déployer sur un cloud et de tirer parti de l'infrastructure matérielle fournie par les fournisseurs de services cloud tels qu'Amazon Web Services (AWS), Google Cloud Platform (GCP) et Microsoft Azure. La plupart des fournisseurs de services cloud populaires disposent de solutions de formation et de déploiement spécialisées pour les modèles ML. Il s'agit notamment d'AutoML sur GCP et d'Amazon SageMaker Autopilot sur AWS.
Lorsque tu déploies des modèles ML on premises (sur ta propre infrastructure matérielle), tu peux utiliser un serveur de modèles préconstruit open source tel que TensorFlow Serving, KServe ou NVIDIA Triton.
Si tu choisis de déployer des modèles ML sur un Cloud, tu peux déployer des modèles entraînés sur des machines virtuelles (VM) telles ...
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