Dans ce cours, vous profiterez de l'expérience d'ingénieurs et de formateurs en ML qui développent des pipelines de ML chez Google Cloud à l'aide de technologies de pointe. Les premiers modules porteront sur TensorFlow Extended (TFX), la plate-forme Google de machine learning de production basée sur TensorFlow et conçue pour gérer des pipelines et des métadonnées de ML. Vous explorerez les composants de pipelines et apprendrez à orchestrer des pipelines avec TFX. Vous verrez également comment automatiser vos pipelines au moyen d'une intégration et d'un déploiement continus, et comment gérer des métadonnées de ML. Ensuite, nous découvrirons comment automatiser et réutiliser des pipelines de ML sur plusieurs frameworks de ML tels que TensorFlow, PyTorch, scikit-learn et XGBoost. Vous apprendrez également à utiliser Cloud Composer, un autre outil Google Cloud, pour orchestrer vos pipelines d'entraînement continu. Enfin, nous verrons comment utiliser MLflow pour gérer l'ensemble du cycle de vie du machine learning.
ML Pipelines on Google Cloud - Français
ML Pipelines on Google Cloud - FrançaisGoogle Cloud
About this Course
What you will learn
Mieux comprendre les composants de pipelines TFX standards
Apprendre à utiliser un contexte interactif TFX pour développer des prototypes de pipelines TFX
Entraîner des modèles TensorFlow, PyTorch, XGBoost et scikit-learn de façon continue avec Kubeflow et AI Platform Pipelines
Effectuer un entraînement continu avec Composer et MLflow
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
Ce module présente le plan du cours.
Présentation des pipelines TFX
Ce module présente TensorFlow Extended (TFX), puis aborde les concepts et les composants TFX.
Orchestrer des pipelines avec TFX
Dans ce module, vous allez :
Composants personnalisés et CI/CD pour les pipelines TFX
Dans ce module, vous allez :
Métadonnées avec TFX
Ce module traite de l'utilisation de métadonnées TFX pour gérer des artefacts.
Effectuer un entraînement continu avec plusieurs SDK, Kubeflow et AI Platform Pipelines
Ce module traite de l'entraînement continu avec plusieurs SDK, mais aussi avec Kubeflow et AI Platform Pipelines.
Effectuer un entraînement continu avec Cloud Composer
Ce module traite de l'entraînement continu avec Cloud Composer.
Pipelines de ML avec MLflow
Ce module présente MLflow et ses composants.
Ce module résume le cours.
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