April 2026
Intermediate
586 pages
17h 40m
French
Content preview from Apprendre AutoML (French Edition)
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Chapitre 15. Étudede cas n° 2 : Commerce de détail —Prévision de la demande omnicanale
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Au chapitre 14, on a vu comment GlobalBank a géré la détection des fraudes en temps réel avec ses exigences strictes en matière de latence et son environnement hostile. On va maintenant s’intéresser à un autre défi : prédire ce que les clients vont acheter, quand ils vont l’acheter et où — aussi bien dans les magasins physiques que sur les canaux numériques.
Remarque
Tous les exemples de code de ce chapitre ont été testés et validés avec AutoGluon 1.4.0. Pour les implémentations complètes, les résultats détaillés, les variantes expérimentales et les techniques avancées supplémentaires, consulte le notebook Jupyter d'accompagnement Supplementary_Code_B_Demand_Forecasting.ipynb.
AutoGluon est en cours de développement. Rends-toi sur le site web d'AutoGluon pour obtenir la documentation la plus récente et les instructions d'installation.
Problématique et contexte
RetailCo, , un détaillant national comptant 450 magasins et une plateforme de commerce électronique en pleine croissance, était confronté à un dilemme classique en matière de stocks : trop de mauvais produits, pas assez de bons. L'entreprise avait 127 millions de dollars immobilisés dans des stocks excédentaires — des références à faible rotation occupant de l'espace dans les entrepôts et nécessitant à ...
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