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= `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/fr/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-93a9289f4e","additionalClasses":"headline-decoration","type":"heading2","lines":["Présentation"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-681cb1ce42","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ESans exagérer, on peut dire que le machine learning a changé le monde. Apprendre aux machines à réfléchir en leur donnant des exemples d’informations que vous voulez qu’elles apprennent (données), au lieu de règles préprogrammées (code) a ouvert la voie à un large éventail d’applications pratiques. Des systèmes de diagnostic en radiologie aux filtres anti-spam pour les e-mails, sans oublier les véhicules semi-autonomes, toutes ces machines ont été entraînées grâce au machine learning (ML).\u003C/p\u003E\n\u003Cp\u003ELe ML est également à l’origine des grands modèles de langage et des applications d’IA générative qui en sont issues. Cependant, créer et entraîner des modèles de ML demande beaucoup de temps et de ressources, ce qui nécessite des investissements importants dans l’infrastructure et une grande expertise en IA. C’est pourquoi une nouvelle catégorie d’outils qui automatisent une grande partie de ces processus a retenu l’attention des data scientists, des ingénieurs, des analystes et des utilisateurs métiers : le machine learning automatisé, ou AutoML. \u003C/p\u003E\n\u003Cp\u003EDans ce guide, nous expliquerons ce qu’est l’AutoML et comment il permet de combler le fossé de connaissances entre les équipes de data science et les utilisateurs lambda, en rendant l’IA plus évolutive et accessible à tous au sein d’une entreprise.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"container_copy_copy__480869401":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1444615495":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__455068363":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__659376865":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1500909857":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1978652079":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"what",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-986979739e","additionalClasses":"headline-decoration","type":"heading2","lines":["Qu’est-ce que l’AutoML ?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-5306213841","text":"\u003Cp\u003EL’AutoML utilise un logiciel pour gérer automatiquement les étapes clés de la création d’un modèle de machine learning, comme la sélection des algorithmes appropriés, le réglage des paramètres du modèle et la transformation des données brutes dans un format que le modèle comprendra, processus connu sous le nom de feature engineering. Cela peut réduire de plusieurs mois à quelques jours, voire quelques heures, le temps nécessaire aux ingénieurs pour créer un modèle simple. L’AutoML démocratise l’IA en permettant aux utilisateurs de secteurs comme \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/healthcare-and-life-sciences/\"\u003Ela santé\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/financial-services/\"\u003Eles services financiers\u003C/a\u003E et \u003Ca href=\"https://www.snowflake.com/fr/solutions/departments/marketing/\"\u003Ele marketing\u003C/a\u003E de construire leurs propres modèles, sans nécessiter d’expertise technique approfondie.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-8f5b0f5a5e","additionalClasses":"headline-decoration","type":"heading2","lines":["Pourquoi l’AutoML change la donne"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-da23bdeeca","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EVoici cinq façons dont l’AutoML révolutionne les règles de construction de modèles :\u003Cbr\u003E\n\u003C/p\u003E\n\u003Cul\u003E\n\u003Cbr _rte_temp_br=\"brEOB\"\u003E\n\u003Cli\u003E\u003Cb\u003EIl démocratise le développement.\u003C/b\u003E L’AutoML élimine les obstacles techniques, de sorte que les experts issus d’un large éventail de secteurs peuvent créer des modèles sophistiqués sans expertise préalable en machine learning.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EIl stimule la productivité.\u003C/b\u003E En automatisant des processus chronophages tels que le feature engineering, la sélection des algorithmes et le réglage des paramètres, l’AutoML réduit drastiquement le temps nécessaire à la création de modèles.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EIl améliore la précision.\u003C/b\u003E Les plateformes d’AutoML testent systématiquement des centaines de combinaisons d’algorithmes et de paramètres et découvrent souvent des modèles plus performants que des humains pourraient manquer.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EIl améliore la reproductibilité.\u003C/b\u003E Les plateformes d’AutoML documentent automatiquement toutes les décisions et tous les paramètres de modélisation. Elles créent ainsi une piste d’audit claire qui permet de reproduire facilement les résultats et de comprendre exactement comment les modèles ont été construits.\u003C/li\u003E\n\u003Cli\u003E\u003Cb style=\"font-family: adobe-clean, "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Ubuntu, "Trebuchet MS", "Lucida Grande", sans-serif;\"\u003EIl assure la constance.\u003C/b\u003E L’AutoML veille à ce que les méthodes de validation, de validation croisée et d’évaluation soient appliquées de manière constante, ce qui réduit les erreurs humaines qui peuvent conduire à des prédictions inexactes.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1444615495":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-4865ca569a","additionalClasses":"headline-decoration","type":"heading2","lines":["Les composants clés de l’AutoML"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-f1a2ad2219","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EVoici les composants clés d’un pipeline d’AutoML :\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EPrétraitement des données\u003C/h4\u003E\n\u003Cp\u003ELors de cette étape, la plateforme nettoie et prépare les données brutes. Elle gère les valeurs manquantes, supprime les valeurs anormales et convertit les types de données dans des formats adaptés aux algorithmes de machine learning, de façon à garantir la qualité et la concordance des données avant le début de l’entraînement des modèles.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EFeature engineering\u003C/h4\u003E\n\u003Cp\u003EEnsuite, la plateforme transforme les données brutes : elle génère de nouvelles variables, code les données catégorielles, dimensionne les fonctionnalités numériques et sélectionne les features les plus pertinentes pour améliorer les prédictions des modèles.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ESélection de modèles\u003C/h4\u003E\n\u003Cp\u003EL’AutoML teste systématiquement plusieurs algorithmes de machine learning (arbres de décision, réseaux neuronaux ou méthodes d’ensemble, par exemple) pour identifier l’approche la plus adaptée à un jeu de données et à un problème spécifiques. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EEntraînement\u003C/h4\u003E\n\u003Cp\u003EL’étape la plus importante consiste à alimenter le modèle avec de grandes quantités de données d’exemple (comme des milliers d’e-mails étiquetés « spam » ou « pas spam ») afin qu’il puisse apprendre à reconnaître les schémas et les relations au sein de ces données. Le modèle peut ensuite utiliser ces schémas appris pour faire des prédictions ou prendre des décisions sur des données jusqu’alors inconnues.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EModélisation d’ensemble\u003C/h4\u003E\n\u003Cp\u003ECette étape consiste à entraîner différents modèles de machine learning sur le même jeu de données, puis à combiner leurs prédictions pour prendre une décision finale. La modélisation d’ensemble produit généralement des résultats plus précis et plus fiables qu’un modèle individuel, car elle réduit l’impact des faiblesses et des biais d’un modèle particulier.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ERéglage des hyperparamètres\u003C/h4\u003E\n\u003Cp\u003EEn réglant automatiquement les paramètres qui contrôlent l’apprentissage de chaque algorithme, comme les taux d’apprentissage, la profondeur des arbres ou les paramètres de régularisation, l’AutoML permet aux utilisateurs d’identifier la meilleure combinaison de paramètres.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EÉvaluation et validation\u003C/h4\u003E\n\u003Cp\u003ELes entreprises ont besoin d’un modèle capable de fonctionner de façon efficace sur des données jusqu’alors inconnues. Les procédures de test, telles que la validation croisée, collectent des indicateurs (exactitude, précision, rappel, etc.) tout en vérifiant les risques de surapprentissage ou « overfitting » (lorsqu’un modèle obtient de mauvais résultats sur des données en dehors de son ensemble d’entraînement) ou de biais.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EDéploiement et surveillance \u003C/h4\u003E\n\u003Cp\u003EL’AutoML identifie automatiquement le modèle le plus performant à des fins de production et met en place des systèmes pour suivre ses performances au fil du temps. Cela permet de s’assurer que le modèle continue à fonctionner efficacement à mesure que les conditions réelles évoluent, afin d’éviter toute dérive du modèle et de déclencher un nouvel entraînement, si nécessaire.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EExplicabilité technique\u003C/h4\u003E\n\u003Cp\u003EDans la mesure du possible, les développeurs veulent pouvoir expliquer pourquoi un modèle a fait une prédiction particulière, afin d’éviter les modèles de type « boîte noire », où le processus de décision est totalement opaque. Les plateformes d’AutoML disposent souvent d’outils qui documentent l’ensemble du processus de modélisation, notamment la façon dont les données ont été prétraitées et la raison pour laquelle certains algorithmes ont été choisis.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1978652079":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-7d25fd9dc7","additionalClasses":"headline-decoration","type":"heading2","lines":["Six cas d’usage courants de l’AutoML"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-1f74bd7f73","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EComme pratiquement tous les secteurs utilisent des modèles de machine learning, l’AutoML peut accélérer les initiatives de ML d’une entreprise dans de nombreux domaines. Voici six cas d’usage courants pour lesquels l’AutoML peut vous aider :\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. Prévision des ventes  \u003C/h4\u003E\n\u003Cp\u003EL’AutoML aide les entreprises à créer des modèles pour analyser l’historique des \u003Ca href=\"https://www.snowflake.com/en/fundamentals/sales-forecasting/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Edonnées de vente\u003C/a\u003E, les tendances saisonnières et les tendances du marché. Les entreprises peuvent rapidement ajuster leurs stocks, leurs effectifs et leurs budgets en fonction de ces \u003Ca href=\"https://www.snowflake.com/en/fundamentals/predictive-analytics-marketing/\"\u003Eprédictions automatisées\u003C/a\u003E, sans avoir à faire appel à des data scientists. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. Détection des fraudes \u003C/h4\u003E\n\u003Cp\u003ELes banques et les organismes de traitement des paiements utilisent le ML pour repérer les potentielles \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003Etransactions frauduleuses\u003C/a\u003E en temps réel. L’AutoML permet aux analystes et aux gestionnaires des risques de fraude de créer des modèles plus rapidement afin de suivre l’évolution des tactiques des fraudeurs. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. Prédiction de la perte de clients \u003C/h4\u003E\n\u003Cp\u003ELes services par abonnement et les opérateurs de \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/telecom/\"\u003Etélécommunication\u003C/a\u003E utilisent le ML pour repérer les clients susceptibles d’annuler leur abonnement, afin de les contacter dans le cadre d’efforts proactifs pour les fidéliser. L’automatisation permet aux entreprises de tester et de déployer rapidement de nouveaux modèles de prédiction de la perte de clients à mesure que le comportement de ces derniers évolue.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E4. Diagnostic des maladies\u003C/h4\u003E\n\u003Cp\u003ELe machine learning aide les professionnels de santé à analyser les images médicales, les résultats de laboratoires et les symptômes des patients pour faciliter les diagnostics et les traitements. À mesure que de nouvelles données sur la recherche médicale et les patients sont disponibles, l’AutoML peut continuellement mettre à jour les modèles existants pour s’assurer que les patients reçoivent les meilleurs soins possibles.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E5. Optimisation des stocks\u003C/h4\u003E\n\u003Cp\u003ELes \u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/retail-consumer-goods/\"\u003Eretailers\u003C/a\u003E utilisent des modèles pour prédire la demande de produits spécifiques sur différents sites, ce qui leur permet de stocker les bons articles au bon moment. L’AutoML peut aider les équipes des opérations de retail à créer des modèles pour différentes catégories de produits ou différents emplacements de magasins, mais aussi à réentraîner automatiquement les modèles à mesure que les conditions du marché évoluent.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E6. Déploiement d’une tarification dynamique\u003C/h4\u003E\n\u003Cp\u003EL’AutoML permet aux plateformes de e-commerce et aux services de covoiturage de déployer des modèles de tarification dynamique grâce à l’intégration automatique des flux de données en temps réel, mais aussi d’expérimenter rapidement différentes stratégies de tarification sur divers marchés, produits ou services. Ainsi, les entreprises peuvent optimiser leurs revenus sans devoir ajuster manuellement leurs tarifs de manière régulière.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__455068363":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-203b3c038f","additionalClasses":"headline-decoration","type":"heading2","lines":["Les principales limites de l’AutoML"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-3a746cd76e","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELes plateformes d’AutoML offrent des avantages dont peuvent profiter toutes les entreprises. Elles peuvent accélérer le développement de modèles, réduire les erreurs humaines, libérer les data scientists pour des tâches plus stratégiques et démocratiser l’accès à l’IA dans toute l’entreprise. Mais elles présentent également certaines limites inhérentes. En voici quelques exemples :\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EElles proposent des solutions génériques\u003C/h4\u003E\n\u003Cp\u003EL’AutoML a tendance à appliquer des approches standard qui ne tiennent pas forcément compte des aspects particuliers de chaque problématique. Cette technologie risque donc de négliger des solutions sur mesure que des experts du domaine pourraient développer pour des secteurs ou des cas d’usage spécifiques.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EElles ont une compréhension limitée des domaines d’activité\u003C/h4\u003E\n\u003Cp\u003ELes systèmes d’AutoML manquent de contexte métier et d’expertise spécialisée pour des secteurs ou des domaines spécifiques. Ils peuvent donc passer à côté de nuances importantes qu’un expert humain pourrait percevoir, comme des tendances saisonnières ou des contraintes réglementaires.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EElles sont tributaires de la qualité des données d’entrée \u003C/h4\u003E\n\u003Cp\u003ELes plateformes d’AutoML ne peuvent pas corriger les données de mauvaise qualité. Si les données d’entrée sont biaisées, incomplètes ou peu pertinentes, les systèmes automatisés généreront des résultats peu fiables.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EElles ne sont pas très flexibles \u003C/h4\u003E\n\u003Cp\u003ELes utilisateurs avancés risquent de rencontrer des difficultés insurmontables s’ils essaient de mettre en œuvre des techniques spécialisées, des algorithmes personnalisés ou des étapes de prétraitement complexes qui ne relèvent pas des capacités automatisées de la plateforme.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003ELes outils de feature engineering peuvent être limités \u003C/h4\u003E\n\u003Cp\u003EBien que les plateformes d’AutoML gèrent le \u003Ca href=\"https://www.snowflake.com/en/fundamentals/feature-engineering-vs-feature-stores/\"\u003Efeature engineering\u003C/a\u003E de base, elles ne sont pas forcément capables de créer des features sophistiquées et spécifiques à un domaine, susceptibles d’améliorer considérablement les performances d’un modèle.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003EElles peuvent fonctionner comme des « boîtes noires »\u003C/h4\u003E\n\u003Cp\u003EMême si une plateforme d’AutoML peut être capable d’expliquer comment un modèle de ML fait des prédictions, les modèles d’ensemble complexes sont parfois beaucoup plus difficiles à interpréter ou à expliquer. 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