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learning","appliedCssClassNames":"summit-page",":type":"snowflake-site/components/structure/page"},{"id":"breadcrumb-729f0e911b-item-8f55d6e367","link":{"valid":true,"url":"/fr/artificial-intelligence/machine-learning/mlops/"},"active":false,"current":false,"title":"MLOps","appliedCssClassNames":"summit-page",":type":"snowflake-site/components/structure/page"},{"id":"breadcrumb-729f0e911b-item-d04dbc66d0","link":{"valid":true,"url":"/fr/artificial-intelligence/machine-learning/mlops/model-deployment/"},"active":true,"current":true,"title":"Déploiement de modèles ML","appliedCssClassNames":"summit-page",":type":"snowflake-site/components/structure/page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_939100716":{"id":"flexible-column-container-f5700f537e","propertiesId":"hub-hero","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-ba3efe51d2","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-aa240b74d6","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Déploiement de modèles ML : de l'entraînement à la production"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-7c78466e97","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003ELe déploiement de modèles de ML est l'étape où un modèle entraîné passe de tests contrôlés aux conditions imprévisibles de la production. Cet article explique comment les équipes peuvent publier des modèles en toute sécurité avec la bonne stratégie de déploiement, la supervision, les pratiques de registre et le plan de retour en arrière.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-62260858ac","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"content_chip":{"id":"content-chip-2c8a750f82","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?quality=85&preferwebp=true","alt":"Laurie MacPherson","lazyEnabled":true,"isLcpImage":true,"width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie MacPherson","Technical Writer, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_copy":{"id":"content-chip-594162ced1","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"709","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--9ad7a3c0-e23e-4370-bc36-f59a5fe02eff/img-1817.jpg?quality=85&preferwebp=true","alt":"Tracy Kabuya","lazyEnabled":true,"isLcpImage":false,"width":"709",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Tracy Kabuya","Contributrice locale, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip","content_chip_copy"],":type":"snowflake-site/components/container"}},":itemsOrder":["title_v2","text","container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-208aea0179",":items":{"youtube":{"id":"embed-db48576bdc","youtubeVideoId":"F34xlRoQ3eQ","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-22311e5e15","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-09a2e8cb2e",":items":{"text_894059747":{"id":"text-d0e00b00b1","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EThèmes MLOps :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-44cc16084e","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/mlops/model-serving/\"\u003EModel serving\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-ab4fb3ff18","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"callout__0":{"id":"text-3555941323","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EDÉFINITION DU DÉPLOIEMENT DE MODÈLES\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003ELe déploiement de modèles de ML est la mise en production encadrée d'un modèle de machine learning validé, avec ses dépendances et sa logique d'inférence, dans un environnement de production où il peut être versionné, exécuté, supervisé et annulé si nécessaire.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-2993bd000a","text":"\u003Cp\u003EComment déployer un modèle en toute sécurité lorsque certaines défaillances n'apparaissent qu'après la mise en production ? L'équipe peut avoir toutes les raisons de faire confiance au modèle candidat : des résultats de validation solides, des comparaisons de référence favorables, des approbations documentées et une version enregistrée prête à être promue.\u003C/p\u003E\r\n\u003Cp\u003EMais la production est un environnement complexe. Les valeurs de caractéristiques peuvent arriver en retard ou dans un format différent, le trafic réel peut révéler des comportements que les données de test n'avaient jamais fait émerger, ou un plan de retour arrière reposant sur des décisions manuelles rapides peut ne pas résister sous la pression. Le déploiement de modèles est l'étape où cette incertitude doit être gérée.\u003C/p\u003E\r\n\u003Cp\u003EUne stratégie de déploiement de modèles solide contrôle l'exposition à la production de la nouvelle version, les signaux que l'équipe supervise et la rapidité avec laquelle le trafic peut revenir au dernier modèle approuvé en cas de dégradation.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-model-deployment":{"id":"title-v2-ac85d4f43c","additionalClasses":"anchor-title anchor-title--definition-deploiement-modeles","type":"heading2","lines":["Qu'est-ce que le déploiement de modèles ?"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-2c2d2f627e","text":"\u003Cp\u003ELe déploiement de modèles est le processus d'intégration d'un \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/\"\u003Emodèle de machine learning\u003C/a\u003E entraîné et validé dans un environnement de production afin qu'il puisse générer des prédictions sur des données réelles. Dans le cycle de vie du machine learning, le déploiement de modèles a lieu après l'entraînement, la validation et l'enregistrement, et avant la mise en service continue, l'inférence et la supervision. Autrement dit : le déploiement gère le processus de publication, la mise en service fournit l'infrastructure d'exécution, et l'inférence est l'acte de générer une prédiction à partir d'une entrée.\u003C/p\u003E\n\u003Cp\u003EDans un workflow \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/mlops/\"\u003EMLOps\u003C/a\u003E typique, le déploiement de modèles s'inscrit dans une séquence :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"callout_model-deployment-makes-release-risk-manageable_0":{"id":"text-6421f51567","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EEntraîner → valider → enregistrer → déployer → mettre en service → superviser\u003C/b\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_what-is-model-deployment_0":{"id":"text-1c30aee2dd","text":"\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EÉtape du cycle de vie MLOps\u003C/th\u003E\r\n\u003Cth\u003ECe qui se passe pendant cette étape\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EEntraînement du modèle\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EUn modèle apprend des régularités à partir de données d'entraînement, en utilisant des caractéristiques, des algorithmes et des paramètres sélectionnés pour produire un artefact de modèle candidat.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EValidation du modèle\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELe modèle candidat est testé par rapport à des données conservées, des modèles de base et des exigences stratégiques pour déterminer s'il est précis, stable et approprié pour la mise en production.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EEnregistrement du modèle\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELe candidat approuvé est ajouté à un registre de modèles avec sa version, ses métadonnées, son lignage, ses métriques, son propriétaire et son statut d'approbation.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EDéploiement des modèles\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELe modèle entraîné et validé est publié dans un environnement de production, avec des contrôles pour le packaging, le routage, la supervision et le retour en arrière.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/mlops/model-serving/\"\u003EMise en service des modèles\u003C/a\u003E\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELe modèle déployé s'exécute dans un environnement d'exécution de production, tel qu'un job de scoring par lots, un point de terminaison en temps réel, une fonction serverless ou un service conteneurisé.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/inference/\"\u003EInférence du modèle\u003C/a\u003E\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELe modèle génère des prédictions à partir d'entrées réelles, soit de manière synchrone pour les cas d'usage en temps réel, soit de manière asynchrone via des workflows en batch ou en file d'attente.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/observability/\"\u003ESupervision des modèles\u003C/a\u003E\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELes équipes suivent le comportement en production, y compris la dérive des entrées, la qualité des prédictions, la latence, les taux d'erreur, les coûts et l'impact stratégique.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ERéentraînement du modèle\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ELe modèle est mis à jour ou reconstruit lorsque la performance se dégrade, que les données changent, que la logique stratégique évolue ou qu'une meilleure version est disponible.\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003ELe déploiement est l'étape où un modèle commence à fonctionner dans un système réel. Le modèle doit être intégré, versionné, validé, connecté aux données de production et acheminé vers le workflow approprié. Pour de nombreuses équipes, en particulier dans les secteurs réglementés, le pipeline de déploiement doit également conserver des preuves : qui a approuvé le modèle, quelle version a été publiée, quels résultats de validation ont soutenu la décision et comment l'équipe peut revenir en arrière si les métriques de production se dégradent.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_types-of-model-deployment":{"id":"title-v2-8cd5799ead","additionalClasses":"anchor-title anchor-title--types-deploiement-modeles","type":"heading2","lines":["Types de déploiement de modèles"],":type":"snowflake-site/components/title-v2"},"text_types-of-model-deployment_0":{"id":"text-61b34a998f","text":"\u003Cp\u003ELes équipes peuvent déployer des modèles de plusieurs manières, selon l'endroit où la prédiction doit apparaître, la rapidité avec laquelle le système en a besoin et la destination de la sortie. Certains modèles évaluent des millions d'enregistrements selon un calendrier et réécrivent les résultats dans une table, tandis que d'autres répondent aux actions des utilisateurs en quelques millisecondes, renvoyant une prédiction pendant qu'une transaction, une recommandation ou une vérification de fraude est encore en cours. Dans d'autres cas, le modèle s'exécute entièrement en dehors d'un environnement centralisé, sur un appareil avec des capacités de calcul, de stockage et de connectivité limitées.\u003C/p\u003E\r\n\u003Ch3\u003EDéploiement en batch\u003C/h3\u003E\r\n\u003Cp\u003EDans le déploiement en batch, un modèle évalue un grand ensemble d'enregistrements selon un calendrier défini ou en réponse à un événement de pipeline de données. Par exemple, un modèle d'attrition peut évaluer tous les clients actifs chaque nuit, ou un modèle de fraude peut analyser les transactions par lots horaires.\u003C/p\u003E\r\n\u003Cp\u003ELe déploiement en batch fonctionne bien lorsque les prédictions n'ont pas besoin d'être renvoyées immédiatement. Il convient également aux workflows où les entrées se trouvent déjà dans des tables analytiques et où la sortie peut être réécrite sous forme de colonne évaluée, de table ou de caractéristique pour une utilisation en aval.\u003C/p\u003E\r\n\u003Cp\u003ELa contrepartie est la latence. Un modèle en batch peut être efficace et plus facile à gouverner, mais ses prédictions ne sont aussi récentes que le calendrier de scoring. Pour les cas d'usage où une décision doit être prise lors d'une session utilisateur, d'une transaction ou d'un événement opérationnel, le déploiement en temps réel est généralement plus adapté.\u003C/p\u003E\r\n\u003Ch3\u003EDéploiement en temps réel\u003C/h3\u003E\r\n\u003Cp\u003ELe déploiement en temps réel, parfois appelé déploiement en ligne, expose un modèle via un point de terminaison à faible latence qui renvoie des prédictions de manière synchrone. Une application envoie une requête, le modèle évalue l'entrée et l'application utilise la sortie pendant que le workflow est encore en cours.\u003C/p\u003E\r\n\u003Cp\u003ECette approche prend en charge des cas d'usage tels que la détection des fraudes lors du paiement, les recommandations de produits lors d'une session de navigation, la tarification dynamique, la personnalisation ou le scoring des risques en temps réel. Étant donné que la prédiction se trouve sur le chemin critique, le déploiement doit tenir compte de la latence, de la disponibilité, de la mise à l'échelle et de la gestion des erreurs.\u003C/p\u003E\r\n\u003Cp\u003EEn pratique, le déploiement en temps réel exige davantage de l'environnement de production. L'équipe doit savoir ce qui se passe lorsque les requêtes augmentent, lorsque le modèle renvoie une erreur, lorsque des caractéristiques sont manquantes ou lorsque la nouvelle version est moins performante pour un sous-ensemble d'utilisateurs. Une stratégie de déploiement telle que le déploiement canari, le déploiement bleu-vert ou le mode shadow, que nous aborderons dans un instant, peut réduire le risque d'exposer le nouveau modèle trop largement avant que son comportement en production ne soit compris.\u003C/p\u003E\r\n\u003Ch3\u003EDéploiement serverless et asynchrone\u003C/h3\u003E\r\n\u003Cp\u003ELe déploiement serverless est utile lorsque les charges de travail sont irrégulières, intermittentes ou difficiles à dimensionner à l'avance. Au lieu d'exécuter une infrastructure dédiée en continu, le modèle s'exécute lorsqu'il est déclenché par un événement, une requête ou une tâche. Cela peut être utile pour les charges de travail où le trafic arrive de manière inégale ou lorsque les équipes souhaitent réduire les frais opérationnels.\u003C/p\u003E\r\n\u003Cp\u003EL'inférence asynchrone convient aux scénarios où une requête ne nécessite pas de réponse immédiate. Un workflow de classification de documents, par exemple, peut accepter un fichier, mettre la requête en file d'attente, exécuter le modèle et réécrire le résultat une fois le traitement terminé. Pour les charges de travail à volume de données élevé, les tâches de longue durée ou les profils de trafic variables, le déploiement asynchrone peut fournir un modèle opérationnel plus propre que de tout forcer via un point de terminaison synchrone.\u003C/p\u003E\r\n\u003Cp\u003EIl est important d'examiner non seulement où le modèle va s'exécuter, mais aussi comment le reste du système va attendre, récupérer et utiliser la sortie. Les files d'attente, les nouvelles tentatives, le suivi du statut et la gestion des défaillances font partie de l'architecture de déploiement.\u003C/p\u003E\r\n\u003Ch3\u003EDéploiement en périphérie\u003C/h3\u003E\r\n\u003Cp\u003EAvec le déploiement en périphérie, le modèle s'exécute sur un appareil ou un environnement local plutôt que dans un système de production centralisé. Un modèle peut s'exécuter sur un téléphone mobile, un capteur, un véhicule, un appareil d'usine ou une application intégrée où la faible latence, le fonctionnement hors ligne ou la localité des données sont importants.\u003C/p\u003E\r\n\u003Cp\u003ELe déploiement en périphérie introduit des contraintes qui n'apparaissent pas toujours dans les environnements centralisés. Le modèle peut devoir être compressé, optimisé pour un calcul limité ou mis à jour via un processus de gestion des appareils. La supervision change également. Si les prédictions se produisent en dehors de la plateforme centrale, les équipes ont besoin d'un moyen de collecter des signaux de performance, de gérer les versions de modèles et de retirer les modèles obsolètes sur une flotte distribuée.\u003C/p\u003E\r\n\u003Cp\u003EPour les cas d'usage réglementés ou sensibles à la sécurité, le déploiement en périphérie soulève également une question de gouvernance : comment l'organisation peut-elle prouver quelle version de modèle était active sur quel appareil à un moment donné ?\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_model-deployment-strategies":{"id":"title-v2-b18b278fb3","additionalClasses":"anchor-title anchor-title--strategies-deploiement-modeles","type":"heading2","lines":["Stratégies de déploiement de modèles"],":type":"snowflake-site/components/title-v2"},"text_model-deployment-strategies_0":{"id":"text-b6e7bcddeb","text":"\u003Cp\u003ELes stratégies de déploiement contrôlent la manière dont une nouvelle version de modèle est exposée à la production. Plutôt que d'envoyer tout le trafic vers un nouveau modèle en une seule fois, les équipes peuvent acheminer le trafic progressivement, comparer les versions ou exécuter le nouveau modèle de manière silencieuse avant que ses sorties n'affectent les décisions réelles. \u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_model-deployment-strategies_0":{"id":"text-f286427d92","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EASTUCE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EChoisissez la stratégie de déploiement en fonction du risque du cas d'usage, et non seulement de la vitesse de publication.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_model-deployment-strategies_1":{"id":"text-334fb6321f","text":"\u003Ch3\u003EDéploiement canari\u003C/h3\u003E\r\n\u003Cp\u003EDans un déploiement canari, un faible pourcentage du trafic de production est d'abord acheminé vers la nouvelle version du modèle. Le modèle existant continue de gérer la majeure partie du trafic pendant que la nouvelle version fait ses preuves face aux entrées en direct.\u003C/p\u003E\r\n\u003Cp\u003EPar exemple, une équipe peut acheminer 5 % des requêtes vers un nouveau modèle de recommandation, comparer le taux de conversion, la latence et les taux d'erreur avec la version actuelle, puis augmenter progressivement le trafic si les résultats se maintiennent. Si les métriques se dégradent, le trafic peut être réacheminé avant que le problème n'affecte l'ensemble de la population en production.\u003C/p\u003E\r\n\u003Cp\u003ELe déploiement canari est utile lorsque les équipes souhaitent obtenir des preuves réelles en production sans basculement complet. Il fonctionne mieux lorsque la supervision est déjà en place et que l'équipe dispose de critères clairs de promotion et de retour en arrière. Sans ces seuils, une version canari peut se transformer en une lente expérience de production sans point de décision évident.\u003C/p\u003E\r\n\u003Ch3\u003EDéploiement blue-green\u003C/h3\u003E\r\n\u003Cp\u003EDans un déploiement blue-green, deux environnements de production existent côte à côte. L'environnement bleu exécute le modèle actuel. L'environnement vert exécute le nouveau modèle. Une fois la nouvelle version validée dans l'environnement vert, le trafic passe du bleu au vert.\u003C/p\u003E\r\n\u003Cp\u003EL'avantage est une séparation nette. L'environnement précédent restant disponible, le retour en arrière peut être rapide : il suffit de réacheminer le trafic vers le bleu. Cela est particulièrement utile lorsque les équipes ont besoin d'un processus de mise en production prévisible et d'une voie de repli claire.\u003C/p\u003E\r\n\u003Cp\u003ELe déploiement blue-green semble être le choix évident, mais il nécessite généralement plus d'infrastructure et une coordination plus étroite. Les deux environnements doivent rester suffisamment alignés pour que le basculement n'introduise pas de dérive de configuration, de problèmes de dépendance ou de différences d'accès aux données.\u003C/p\u003E\r\n\u003Ch3\u003EDéploiement shadow\u003C/h3\u003E\r\n\u003Cp\u003EEn déploiement shadow, le nouveau modèle reçoit une copie du trafic de production en direct, mais ses prédictions ne sont pas transmises aux utilisateurs ni aux systèmes en aval. Le modèle actuel prend toujours la décision de production, le nouveau modèle s'exécutant silencieusement en parallèle.\u003C/p\u003E\r\n\u003Cp\u003ELes équipes choisissent souvent cette stratégie lorsqu'elles souhaitent observer le comportement d'un modèle sur des entrées en direct avant de faire confiance à ses sorties. Un modèle de fraude, par exemple, pourrait évaluer des transactions réelles en mode shadow pendant que les analystes comparent ses prédictions avec le modèle de production actuel et les résultats finaux.\u003C/p\u003E\r\n\u003Cp\u003ELe mode shadow est particulièrement précieux pour les cas d'usage à haut risque, car il fournit aux équipes des preuves de type production sans modifier l'expérience utilisateur ou la décision métier. Le compromis est qu'il peut être plus complexe à exploiter. Les équipes doivent dupliquer les entrées, capturer les sorties et comparer les résultats sans introduire de latence ni perturber les systèmes en aval.\u003C/p\u003E\r\n\u003Ch3\u003ETests A/B et déploiement champion-challenger\u003C/h3\u003E\r\n\u003Cp\u003ELes tests A/B comparent les versions de modèles en attribuant différents utilisateurs, requêtes ou segments à différentes variantes. Contrairement au mode shadow, la sortie du nouveau modèle affecte l'expérience de production pour le groupe qui lui est assigné.\u003C/p\u003E\r\n\u003Cp\u003EUn schéma champion-challenger est une approche connexe. Le modèle de production actuel est le champion, et un ou plusieurs nouveaux modèles agissent comme challengers. Les challengers sont évalués par rapport au champion à l'aide du trafic en direct, de métriques métier ou de données de résultats différés. Si un challenger est plus performant et répond aux exigences opérationnelles, il peut remplacer le champion.\u003C/p\u003E\r\n\u003Cp\u003EPour certains cas d'usage, les équipes peuvent également utiliser des stratégies de bandit manchot, qui allouent dynamiquement plus de trafic aux variantes les plus performantes au fil du temps. Cette approche peut être utile lorsque les équipes souhaitent optimiser tout en testant, mais elle nécessite une conception minutieuse des métriques. Si le bandit optimise pour le mauvais résultat, le processus de déploiement peut acheminer davantage de trafic vers un modèle qui semble performant selon un critère tout en créant des problèmes en aval ailleurs.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-model-deployment-pipeline":{"id":"title-v2-d417040602","additionalClasses":"anchor-title anchor-title--pipeline-deploiement-modeles","type":"heading2","lines":["Le pipeline de déploiement de modèles"],":type":"snowflake-site/components/title-v2"},"text_the-model-deployment-pipeline_0":{"id":"text-809d5c2b36","text":"\u003Cp\u003EUne stratégie de déploiement contrôle l'exposition au trafic, mais le pipeline de déploiement contrôle la façon dont une version de modèle passe de l'entraînement à la production. Dans les environnements de ML matures, ce pipeline est reproductible, auditable et connecté au registre de modèles.\u003C/p\u003E\r\n\u003Cp\u003EUn pipeline de déploiement de modèles typique comprend les étapes suivantes :\u003C/p\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003E\u003Cb\u003EPackager le modèle :\u003C/b\u003E l'artefact du modèle, les dépendances, les exigences d'exécution et le code d'inférence sont préparés pour le déploiement. Selon l'environnement, cela peut impliquer la conteneurisation, la sérialisation ou l'intégration du modèle avec sa logique de prétraitement.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EEnregistrer le modèle :\u003C/b\u003E le modèle est consigné dans un registre de modèles avec sa version, ses métadonnées, son contexte d'entraînement, ses métriques d'évaluation et son lignage. Le registre sert de point de transfert entre le développement et le déploiement.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPréparer et valider le candidat :\u003C/b\u003E avant la mise en production, la version candidate est vérifiée dans un environnement de staging. Les tests peuvent couvrir le schéma d'entrée, la disponibilité des caractéristiques, la latence, la sécurité, l'équité, l'explicabilité ou les critères d'acceptation spécifiques au métier.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EApprouver la mise en production :\u003C/b\u003E dans les environnements gouvernés, une version de modèle nécessite souvent la validation d'un propriétaire de modèle, d'une équipe de \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/ai-risk-management/\"\u003Egestion des risques\u003C/a\u003E, d'un data steward ou d'un responsable de la conformité. Le workflow de validation doit consigner qui a validé le modèle, à quel moment et quelles pièces justificatives ont été examinées.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDéployer le modèle :\u003C/b\u003E le modèle est publié dans l'environnement de service cible, tel qu'un pipeline de scoring par lots, un point de terminaison en temps réel, une fonction serverless ou un appareil en périphérie.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAcheminer le trafic :\u003C/b\u003E selon la stratégie, le trafic peut basculer d'un coup, progressivement via un déploiement canari, de manière atomique via un basculement blue-green ou silencieusement via le mode shadow.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESurveiller le comportement en production :\u003C/b\u003E après la publication, la supervision suit les métriques techniques, la qualité du modèle et les résultats métier. La latence, le taux d'erreur, la dérive, la distribution des prédictions, la qualité des caractéristiques et les métriques de résultats peuvent tous indiquer si le déploiement fonctionne comme prévu.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ERevenir en arrière ou promouvoir :\u003C/b\u003E si la nouvelle version est performante, elle peut recevoir plus de trafic ou remplacer le modèle précédent. Si elle se dégrade, l'équipe a besoin d'une voie de retour en arrière qui restaure proprement la version précédente.\u003C/li\u003E\r\n\u003C/ol\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_the-model-deployment-pipeline_0":{"id":"text-722460a086","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EASTUCE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ELe CI/CD pour le ML peut automatiser une grande partie de ce processus, mais une automatisation qui fonctionne de manière indépendante peut poser problème : un pipeline qui publie rapidement des modèles mal gouvernés est un risque. Une meilleure approche consiste à combiner l'automatisation avec des points de validation, des artefacts versionnés, le lignage, les résultats de tests et des règles de retour arrière.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_rollback-governance-the-part-teams-often-underdesign":{"id":"title-v2-642be3ce2c","additionalClasses":"anchor-title anchor-title--gouvernance-retour-arriere","type":"heading2","lines":["Gouvernance du retour en arrière : la partie que les équipes sous-estiment souvent"],":type":"snowflake-site/components/title-v2"},"text_rollback-governance-the-part-teams-often-underdesign_0":{"id":"text-0c99027eea","text":"\u003Cp\u003ELe retour en arrière est souvent décrit comme une action technique : réacheminer le trafic vers le modèle précédent. Mais en pratique, c'est aussi un processus de \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/ai-governance/model-governance/\"\u003Egouvernance\u003C/a\u003E.\u003C/p\u003E\r\n\u003Cp\u003EUn plan de retour en arrière solide répond à plusieurs questions préparatoires. Quelles métriques déclenchent le retour en arrière ? Qui a l'autorité pour l'approuver ? Quelle version du modèle est la solution de repli connue et fiable ? Qu'advient-il des prédictions déjà produites par le modèle dégradé ? L'équipe doit-elle informer les parties prenantes en aval, conserver des preuves ou documenter une exception à la politique ?\u003C/p\u003E\r\n\u003Cp\u003EPour un modèle de personnalisation à faible risque, le retour en arrière peut dépendre du taux de conversion, de la latence et des seuils d'erreur. Pour un modèle de crédit, de santé ou d'assurance, le retour en arrière pourrait également nécessiter un enregistrement de la personne qui a pris la décision, des preuves qui l'ont étayée et de la manière dont l'organisation a géré les sorties affectées.\u003C/p\u003E\r\n\u003Cp\u003ELe registre de modèles joue ici un rôle important. Lorsque chaque version de modèle est enregistrée avec ses métadonnées, son lignage, son état d'approbation et son statut de déploiement, l'équipe peut identifier quelle version est en production et quelle version doit la remplacer lors du retour en arrière. Sans cette source de vérité, le retour en arrière peut dépendre de connaissances informelles détenues par quelques personnes, d'anciens tickets ou d'une reconstruction manuelle lors d'un problème de production.\u003C/p\u003E\r\n\u003Cp\u003EUn workflow de retour en arrière solide comprend généralement :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EUn déclencheur clair, tel qu'une précision dégradée, une dérive, une latence, un taux d'erreur ou un mouvement de KPI business\u003C/li\u003E\r\n\u003Cli\u003EUne version antérieure connue et fiable, conservée dans le registre de modèles\u003C/li\u003E\r\n\u003Cli\u003EUn mécanisme d'acheminement du trafic capable de restaurer rapidement la version précédente\u003C/li\u003E\r\n\u003Cli\u003EUn enregistrement d'audit indiquant qui a initié le retour en arrière et pourquoi\u003C/li\u003E\r\n\u003Cli\u003EUn examen post-retour arrière des prédictions affectées, des systèmes en aval et des lacunes de supervision\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EL'objectif n'est pas de rendre chaque déploiement sans risque : c'est impossible. Mais les équipes peuvent rendre le risque visible, limité et réversible.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_rollback-governance-the-part-teams-often-underdesign_0":{"id":"text-6bce6dea48","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EPIÈGE COURANT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ELes équipes sous-estiment souvent le retour en arrière, en supposant qu'elles peuvent simplement revenir au modèle précédent sans définir de déclencheurs, de propriété, d'exigences d'audit ou de la manière de gérer les prédictions affectées.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_model-deployment-best-practices":{"id":"title-v2-fc9cec6d5a","additionalClasses":"anchor-title anchor-title--bonnes-pratiques-deploiement-modeles","type":"heading2","lines":["Bonnes pratiques de déploiement de modèles"],":type":"snowflake-site/components/title-v2"},"text_model-deployment-best-practices_0":{"id":"text-e8b93887e0","text":"\u003Cp\u003EUn pipeline bien conçu peut mettre des modèles en production rapidement, car les vérifications, validations et chemins de retour arrière nécessaires sont déjà définis.\u003C/p\u003E\r\n\u003Ch3\u003EPartez d’un objectif de déploiement, pas seulement d’un artefact de modèle\u003C/h3\u003E\r\n\u003Cp\u003EAvant de préparer le modèle pour son déploiement, définissez comment ses prédictions seront consommées. Un modèle qui écrit des scores quotidiens dans une table, par exemple, a des exigences différentes de celles d'un modèle qui répond à un événement de paiement en 50 millisecondes. L'objectif de déploiement détermine la stratégie d'environnement d'exécution, de supervision, de retour arrière et de tests.\u003C/p\u003E\r\n\u003Ch3\u003EMaintenez l'alignement du prétraitement et de la logique des caractéristiques\u003C/h3\u003E\r\n\u003Cp\u003EDe nombreux échecs de déploiement proviennent d'inadéquations entre les entrées d'entraînement et de production. Par exemple, une caractéristique calculée d'une certaine manière pendant l'entraînement et d'une autre manière lors de l'inférence peut modifier le comportement du modèle sans modifier le fichier du modèle lui-même. Intégrez la logique de prétraitement au modèle dans la mesure du possible, ou utilisez des pipelines de caractéristiques gouvernés qui maintiennent la cohérence des définitions entre l'entraînement et la production.\u003C/p\u003E\r\n\u003Ch3\u003EUtilisez le registre de modèles comme point de transfert\u003C/h3\u003E\r\n\u003Cp\u003EUn registre de modèles doit capturer les versions de modèles, les propriétaires, les métadonnées, les métriques, le lignage, le statut d'approbation et l'état de déploiement. Lorsque le registre sert de point de transfert entre l'entraînement et le déploiement, les équipes peuvent publier des modèles avec un historique plus clair de ce qui a été approuvé et de ce qui a changé.\u003C/p\u003E\r\n\u003Ch3\u003EAdaptez la stratégie de déploiement au risque\u003C/h3\u003E\r\n\u003Cp\u003EUn modèle batch interne à faible risque peut ne nécessiter qu'un simple déploiement planifié avec supervision et retour en arrière, tandis qu'un modèle en temps réel orienté client peut nécessiter un déploiement canari, un mode shadow ou une version blue-green. Dans les environnements réglementés, les workflows d'approbation et les preuves d'audit doivent s'intégrer au processus de mise en production plutôt qu'en parallèle.\u003C/p\u003E\r\n\u003Ch3\u003EDéfinissez le retour en arrière avant l'arrivée du trafic de production\u003C/h3\u003E\r\n\u003Cp\u003ELe retour en arrière ne doit pas s'improviser. Avant la mise en production, identifiez la version de secours, le déclencheur de retour en arrière, le responsable de la décision et le mécanisme de routage du trafic. Pour les modèles dont les résultats affectent des décisions réglementées, incluez également les exigences en matière de preuves.\u003C/p\u003E\r\n\u003Ch3\u003ESupervisez le modèle et le système qui l'entoure\u003C/h3\u003E\r\n\u003Cp\u003EUn modèle déployé échoue généralement parce que le modèle se dégrade, que les données changent ou que le système qui l'entoure est sous pression. Supervisez la qualité du modèle, la dérive des entrées, la distribution des prédictions, la fraîcheur des caractéristiques, la latence, le débit et les taux d'erreur. Lorsque les résultats arrivent plus tard, utilisez des métriques proxy jusqu'à ce que la vérité terrain soit disponible.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_model-deployment-best-practices_0":{"id":"text-1dd426b952","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EPIÈGE COURANT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ELes équipes considèrent parfois l'artefact du modèle comme la seule chose déployée, tout en négligeant la logique des caractéristiques, les étapes de prétraitement et les dépendances qui façonnent ses prédictions. Même un modèle validé peut se comporter de manière imprévisible en production si ces éléments environnants diffèrent de l'environnement d'entraînement.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_deploying-models-with-snowflake":{"id":"title-v2-75815c6ae6","additionalClasses":"anchor-title anchor-title--deployer-modeles-snowflake","type":"heading2","lines":["Déployer des modèles avec Snowflake"],":type":"snowflake-site/components/title-v2"},"text_deploying-models-with-snowflake_0":{"id":"text-cb0f2af9a7","text":"\u003Cp\u003ESnowflake prend en charge des approches de déploiement de modèles qui aident les équipes à rapprocher les modèles, les données et les contrôles de gouvernance. Avec \u003Ca href=\"https://docs.snowflake.com/fr/developer-guide/snowflake-ml/model-registry/overview\"\u003ESnowflake Model Registry\u003C/a\u003E, les équipes peuvent gérer les modèles pris en charge et les métadonnées associées dans Snowflake, notamment les informations de version, les métriques, le lignage et le statut de validation à mesure que les modèles passent du développement à la production.\u003C/p\u003E\r\n\u003Cp\u003EPour l'inférence, Snowflake propose des options de déploiement à travers les environnements d'exécution pris en charge, notamment l'exécution sur entrepôt et \u003Ca href=\"https://docs.snowflake.com/fr/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services\u003C/a\u003E. 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