Ce cours présente une approche pratique du workflow de ML avec une étude de cas dans laquelle une équipe est confrontée à plusieurs exigences métier et cas d'utilisation de ML. Cette équipe doit comprendre quels outils sont nécessaires pour gérer et gouverner les données, et trouver la meilleure approche pour prétraiter les données, de la découverte de Dataflow et Dataprep à l'utilisation de BigQuery pour les opérations de prétraitement.
Machine Learning in the Enterprise - Français
Machine Learning in the Enterprise - FrançaisGoogle Cloud
About this Course
What you will learn
Décrire les options de gestion, de gouvernance et de prétraitement des données
Identifier quand utiliser Vertex AutoML, BigQuery ML, et la formation personnalisée
Mise en place de l'hyperparamètre Vertex Vizier Tuning
Expliquer comment créer des prédictions par lots et en ligne, configurer la surveillance des modèles et créer des pipelines à l'aide de Vertex AI
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
Module 0: Présentation
Ce module présente le cours et ses objectifs.
Module 1: Comprendre le workflow de ML en entreprise
Ce module traite du workflow de ML en entreprise et de l'objectif de chaque étape.
Ce module passe en revue les outils Google de gestion et de gouvernance des données d'entreprise : Feature Store, Data Catalog, Dataplex et Analytics Hub.
Module 3: Science du machine learning et entraînement personnalisé
Ce module passe en revue l'art et la science du machine learning et des réseaux de neurones. Nous verrons également comment entraîner des modèles de ML personnalisés à l'aide de Vertex AI.
Module 4: Réglages d'hyperparamètres à l'aide de Vertex Vizier
Dans ce module, nous verrons comment régler les hyperparamètres à l'aide de Vertex AI Vizier.
Module 5: Prédiction et surveillance des modèles avec Vertex AI
Ce module porte sur la prédiction et la surveillance des modèles avec Vertex AI. Nous aborderons d'abord les prédictions par lot et en ligne à l'aide de conteneurs prédéfinis et personnalisés, puis nous passerons en revue la surveillance des modèles, un service permettant de gérer les performances de vos modèles de ML.
Vertex AI Pipelines
Ce module traite des pipelines Vertex AI et de la manière dont ils doivent être créés pour orchestrer votre workflow de ML.
Module 7: Bonnes pratiques pour le développement du ML
Ce module passe en revue les bonnes pratiques pour différents processus liés au machine learning dans Vertex AI.
Module 8: Résumé du cours
Ce module est un résumé du cours "Machine Learning in the Enterprise".
Module 9: Résumé de la série
Ce module est un résumé de la série de cours "Machine Learning on Google Cloud".
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Last Updated: September 1, 2015
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