{"id":203658,"date":"2023-06-16T10:45:39","date_gmt":"2023-06-16T10:45:39","guid":{"rendered":"https:\/\/datascientest.com\/?p=203658"},"modified":"2023-11-09T14:21:53","modified_gmt":"2023-11-09T14:21:53","slug":"aws-sagemaker-tout-savoir","status":"publish","type":"post","link":"https:\/\/datascientest.com\/aws-sagemaker-tout-savoir","title":{"rendered":"AWS SageMaker : Guide d&rsquo;utilisation de la plateforme"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"203658\" class=\"elementor elementor-203658\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5500d03 e-flex e-con-boxed e-con\" data-id=\"5500d03\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-fa729db elementor-widget elementor-widget-heading\" data-id=\"fa729db\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Amazon SageMaker est une plateforme cloud con\u00e7ue par Amazon Web Services (AWS) particuli\u00e8rement utile pour la Data Science. D\u00e9couvrez ci-dessous plus de d\u00e9tails sur les fonctionnalit\u00e9s principales ainsi que l'utilisation des outils de la plateforme en allant du d\u00e9veloppement d\u2019un mod\u00e8le de Machine Learning jusqu\u2019\u00e0 son d\u00e9ploiement.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4a37863 e-flex e-con-boxed e-con\" data-id=\"4a37863\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a960633 elementor-widget elementor-widget-heading\" data-id=\"a960633\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Introduction<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eb0dba0 elementor-widget elementor-widget-text-editor\" data-id=\"eb0dba0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">Amazon SageMaker est une plateforme cloud bas\u00e9e sur le Machine Learning qui constitue un service de la plateforme cloud <a href=\"https:\/\/datascientest.com\/amazon-web-services-tout-savoir\">Amazon Web Services<\/a> (AWS). AWS dispose de nombreux services sur internet et peut \u00eatre utilis\u00e9 pour g\u00e9rer une application cloud. <\/span><\/p><p><span style=\"font-weight: 400;\">SageMaker <strong>permet \u00e0 un utilisateur de cr\u00e9er et de d\u00e9ployer des mod\u00e8les de Machine Learning<\/strong> dans un environnement de production \u00e0 une grande \u00e9chelle. Ainsi, en utilisant cette plateforme, un data scientist peut acc\u00e9der \u00e0 des outils puissants pour r\u00e9soudre des probl\u00e8mes de classification, de r\u00e9gression, de clustering, etc. Sagemaker permet ainsi aux entreprises d&rsquo;acc\u00e9l\u00e9rer le processus de d\u00e9veloppement de leurs mod\u00e8les \u00e0 travers la r\u00e9duction des co\u00fbts et l\u2019am\u00e9lioration de l\u2019efficacit\u00e9 op\u00e9rationnelle gr\u00e2ce \u00e0 ses multiples fonctionnalit\u00e9s.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e47b120 e-flex e-con-boxed e-con\" data-id=\"e47b120\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7303aed elementor-widget elementor-widget-heading\" data-id=\"7303aed\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Les fonctionnalit\u00e9s principales de SageMaker<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8adce34 elementor-widget elementor-widget-text-editor\" data-id=\"8adce34\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Tout d\u2019abord, pour utiliser la plateforme SageMaker, l\u2019utilisateur dispose de deux possibilit\u00e9s :<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Utiliser une instance de notebook \u00e0 travers l\u2019environnement SageMaker Notebooks Instances pour ex\u00e9cuter des notebooks <a href=\"https:\/\/datascientest.com\/jupyter-notebook-tout-savoir\">Jupyter<\/a>. Cet environnement comprend des biblioth\u00e8ques usuelles de Machine Learning et des frameworks.<\/span><\/li><li>Utiliser SageMaker Studio qui constitue <strong>un environnement de d\u00e9veloppement de Machine Learning int\u00e9gr\u00e9 et bas\u00e9 sur le cloud<\/strong> permettant ainsi \u00e0 un utilisateur de g\u00e9rer l\u2019ensemble du processus de d\u00e9veloppement d\u2019un mod\u00e8le \u00e0 travers une interface utilisateur constitu\u00e9e de nombreux outils et de fonctionnalit\u00e9s avanc\u00e9es. Par exemple, <b>SageMaker Studio<\/b> est constitu\u00e9 entre autres de notebooks int\u00e9gr\u00e9s mais \u00e9galement d\u2019outils pour g\u00e9rer les donn\u00e9es et les mod\u00e8les (entra\u00eenement, d\u00e9ploiement et surveillance). SageMaker Studio offre donc une exp\u00e9rience plus compl\u00e8te pour le d\u00e9veloppement de\u00a0<a href=\"https:\/\/datascientest.com\/machine-learning-tout-savoir\" target=\"_blank\" rel=\"noopener\">Machine Learning<\/a>\u00a0que l\u2019environnement SageMaker Notebook. Le choix revient donc \u00e0 l\u2019utilisateur et il doit \u00eatre adapt\u00e9 au projet qu\u2019il souhaite r\u00e9aliser.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7d93a63 elementor-widget elementor-widget-image\" data-id=\"7d93a63\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1340\" height=\"459\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image1-6.png\" class=\"attachment-full size-full wp-image-203672\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d4e51ce elementor-widget elementor-widget-image\" data-id=\"d4e51ce\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1472\" height=\"473\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image6-1.png\" class=\"attachment-full size-full wp-image-203673\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image6-1.png 1472w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image6-1-300x96.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image6-1-1024x329.png 1024w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image6-1-768x247.png 768w\" sizes=\"(max-width: 1472px) 100vw, 1472px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-52bd7a1 elementor-widget elementor-widget-text-editor\" data-id=\"52bd7a1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Une fois le projet cr\u00e9\u00e9, l\u2019utilisateur dispose de multiples fonctionnalit\u00e9s vari\u00e9es avec l\u2019outil Sagemaker pour d\u00e9velopper son projet de Machine Learning. Ces fonctionnalit\u00e9s incluent par exemple :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">La pr\u00e9paration des donn\u00e9es.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">L\u2019entra\u00eenement des mod\u00e8les \u00e0 travers des algorithmes de Machine Learning pr\u00e9construits par AWS SageMaker ou bien l\u2019utilisation des biblioth\u00e8ques (libraries) usuelles comme <a href=\"https:\/\/datascientest.com\/pytorch-tout-savoir\">Pytorch<\/a>, <a href=\"https:\/\/datascientest.com\/tensorflow\">TensorFlow<\/a> et <a href=\"https:\/\/datascientest.com\/tout-savoir-sur-scikit-learn\">Scikit Learn<\/a>.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Le d\u00e9ploiement des mod\u00e8les.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Le monitoring des mod\u00e8les gr\u00e2ce \u00e0 l\u2019\u00e9tude des performances d\u2019un mod\u00e8le en temps r\u00e9el \u00e0 travers des analyses de performances et la d\u00e9tection d\u2019anomalies.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">La gestion des ressources.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-37a8491 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"37a8491\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-aws\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Ma\u00eetriser les fonctionnalit\u00e9s d'Amazon SageMaker<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b0b421f e-flex e-con-boxed e-con\" data-id=\"b0b421f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2f786a6 elementor-widget elementor-widget-heading\" data-id=\"2f786a6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Comment \u00e9tablir et d\u00e9ployer un mod\u00e8le de Machine Learning avec SageMaker ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c423bad elementor-widget elementor-widget-text-editor\" data-id=\"c423bad\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Afin d\u2019\u00e9tablir et mettre en production un mod\u00e8le de Machine Learning en utilisant \u00e0 son plein potentiel SageMaker, des \u00e9tapes sont \u00e0 r\u00e9aliser dans un ordre pr\u00e9cis comme cela est d\u00e9crit ci-dessous :\u00a0 <\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-87f99df elementor-widget elementor-widget-heading\" data-id=\"87f99df\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">1. Pr\u00e9paration des donn\u00e9es<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0dcb1ae elementor-widget elementor-widget-text-editor\" data-id=\"0dcb1ae\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">La premi\u00e8re \u00e9tape consiste \u00e0 pr\u00e9parer les donn\u00e9es, elle peut \u00eatre optimis\u00e9e \u00e0 partir de diff\u00e9rents outils issus de la plateforme :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Sagemaker Clarify<\/strong> : Cet outil est capable de d\u00e9tecter les biais pr\u00e9sents dans les donn\u00e9es d&rsquo;entra\u00eenement pour s\u2019assurer que le mod\u00e8le soit suffisamment pr\u00e9cis. Il propose \u00e9galement des strat\u00e9gies pour corriger ces biais.\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Ground Truth<\/strong> : On utilise cette feature pour annoter des ensembles de donn\u00e9es n\u00e9cessaires \u00e0 l&rsquo;entra\u00eenement de mod\u00e8les de Machine Learning. Cela permet ainsi de r\u00e9duire les co\u00fbts li\u00e9s \u00e0 la labellisation des donn\u00e9es.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Data Wrangler<\/strong> : Cette fonctionnalit\u00e9 r\u00e9alise un nettoyage, une normalisation et une transformation des donn\u00e9es brutes en un ensemble de donn\u00e9es utilisables pour l\u2019entra\u00eenement d\u2019un mod\u00e8le. L\u2019utilisateur dispose \u00e9galement d\u2019une interface graphique pour faciliter la pr\u00e9paration des donn\u00e9es.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>ML g\u00e9ospatial<\/strong> : Cet outil permet l\u2019analyse et le traitement de donn\u00e9es g\u00e9ospatiales \u00e0 une grande \u00e9chelle.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Feature Store<\/strong> : Gr\u00e2ce \u00e0 ce service, l\u2019utilisateur peut stocker, partager et g\u00e9rer les features utilis\u00e9es dans ses mod\u00e8les.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-582e78f elementor-widget elementor-widget-image\" data-id=\"582e78f\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1370\" height=\"459\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image4-3.png\" class=\"attachment-full size-full wp-image-203680\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image4-3.png 1370w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image4-3-300x101.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image4-3-1024x343.png 1024w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image4-3-768x257.png 768w\" sizes=\"(max-width: 1370px) 100vw, 1370px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20b4939 elementor-widget elementor-widget-heading\" data-id=\"20b4939\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">2. Entra\u00eenement du mod\u00e8le<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-13447f8 elementor-widget elementor-widget-text-editor\" data-id=\"13447f8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Concernant l\u2019entra\u00eenement d\u2019un mod\u00e8le, l\u2019utilisateur dispose de multiples outils gr\u00e2ce \u00e0 la plateforme comme :\u00a0<\/span><\/p><ul><li aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Autopilot<\/strong> : Cet outil permet de faire du d\u00e9veloppement de Machine Learning de fa\u00e7on automatis\u00e9e. Il permet \u00e0 un utilisateur non exp\u00e9riment\u00e9 de cr\u00e9er un mod\u00e8le de Machine Learning simplement. En effet, apr\u00e8s avoir indiqu\u00e9 toutes les donn\u00e9es \u00e0 <\/span><b>SageMaker Autopilot<\/b><span style=\"font-weight: 400;\">, l\u2019outil construit et optimise automatiquement un mod\u00e8le. \u00c0 travers cet outil, l\u2019utilisateur peut \u00e9galement d\u00e9couvrir comment le mod\u00e8le prend des d\u00e9cisions.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Debugger<\/strong> : Avec cette feature, l\u2019utilisateur peut d\u00e9boguer des mod\u00e8les afin d\u2019obtenir des informations sur les erreurs de calcul et les probl\u00e8mes de convergence. Cela est tr\u00e8s utile pour savoir si le mod\u00e8le est bien adapt\u00e9 aux donn\u00e9es.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Experiments<\/strong> : Avec cette fonctionnalit\u00e9, le d\u00e9veloppeur peut \u00e9tudier, comparer des mod\u00e8les et observer la journalisation des m\u00e9triques.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Automatic Model tuning<\/strong> : Cet outil est capable d\u2019automatiser le choix des hyperparam\u00e8tres optimaux pour un mod\u00e8le \u00e0 travers des techniques de recherches vari\u00e9es.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Distributed Training <\/strong>: Cette feature est utile pour r\u00e9duire fortement le temps d\u2019entra\u00eenement des mod\u00e8les \u00e0 travers la parall\u00e9lisation des processus d\u2019entra\u00eenement sur plusieurs instances.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>SageMaker Canvas<\/strong> : Il s\u2019agit d\u2019un environnement visuel utilis\u00e9 pour cr\u00e9er, g\u00e9rer et visualiser des workflows de Machine Learning. Un <a href=\"https:\/\/datascientest.com\/workflow-tout-savoir\">workflow<\/a> est un outil utilis\u00e9 pour l\u2019organisation et la gestion des \u00e9tapes de d\u00e9veloppement d\u2019un mod\u00e8le. Il est \u00e9galement possible de partager ses workflows \u00e0 d\u2019autres utilisateurs pour collaborer sur des projets. <\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bc0911b elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"bc0911b\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-aws\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Apprendre \u00e0 entra\u00eener un mod\u00e8le avec Amazon SageMaker<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2cb1e24 elementor-widget elementor-widget-image\" data-id=\"2cb1e24\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"871\" height=\"409\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image2-5.png\" class=\"attachment-full size-full wp-image-203684\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image2-5.png 871w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image2-5-300x141.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image2-5-768x361.png 768w\" sizes=\"(max-width: 871px) 100vw, 871px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1b0ede8 elementor-widget elementor-widget-heading\" data-id=\"1b0ede8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">3. D\u00e9ploiement du mod\u00e8le<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e25f70c elementor-widget elementor-widget-text-editor\" data-id=\"e25f70c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Afin de d\u00e9ployer son mod\u00e8le, la fonction principale que l\u2019utilisateur peut utiliser est <strong>SageMaker Edge<\/strong>. Cette fonction peut d\u00e9ployer des mod\u00e8les de Machine Learning sur des appareils et des applications Edge \u00e0 travers un kit de d\u00e9veloppement logiciel (SDK) qui simplifie l\u2019int\u00e9gration des mod\u00e8les d\u00e9ploy\u00e9s.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Les appareils Edge sont des dispositifs mat\u00e9riels constitu\u00e9s entre autres de capteurs, de processeurs et de logiciels d\u2019exploitations utilis\u00e9s pour la collecte, le traitement et le stockage de donn\u00e9es en local. En exemple d\u2019appareils Edge, on peut citer les cam\u00e9ras de s\u00e9curit\u00e9, les montres connect\u00e9es, les voitures autonomes et les thermostat intelligents&#8230;). Les applications Edge, quant \u00e0 elles, sont des applications logicielles con\u00e7ues pour \u00eatre utilis\u00e9es sur des appareils Edge. Elles peuvent \u00eatre utilis\u00e9es pour traiter les donn\u00e9es, optimiser des performances, prendre des d\u00e9cisions\u2026<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-470ca30 elementor-widget elementor-widget-heading\" data-id=\"470ca30\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">4. Maintenance du mod\u00e8le d\u00e9ploy\u00e9<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f13cbb9 elementor-widget elementor-widget-text-editor\" data-id=\"f13cbb9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Une fois le d\u00e9ploiement r\u00e9alis\u00e9, l\u2019utilisateur peut appliquer les fonctions suivantes sur ses mod\u00e8les d\u00e9ploy\u00e9s :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker Model Monitor : Cette fonctionnalit\u00e9 permet de surveiller la performance des mod\u00e8les de Machine Learning qui ont \u00e9t\u00e9 d\u00e9ploy\u00e9s en production. Cet outil peut d\u00e9tecter des erreurs de pr\u00e9dictions, des diminutions de performances et des irr\u00e9gularit\u00e9s relatives \u00e0 la distribution des donn\u00e9es. Le dernier atout de cet outil est l\u2019envoi d\u2019alertes \u00e0 l\u2019utilisateur afin qu\u2019il mette en place des mesures correctives.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker Pipelines : Ce service est utilis\u00e9 pour la cr\u00e9ation et la gestion des workflows de Machine Learning. Pour cela, l\u2019utilisateur dispose d\u2019un environnement pour construire, valider et d\u00e9ployer les workflows gr\u00e2ce \u00e0 des conteneurs <a href=\"https:\/\/datascientest.com\/docker-guide-complet\">Docker<\/a>.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker Test Shadow : Cette fonction peut simuler des pr\u00e9dictions de mod\u00e8les de Machine Learning dans un environnement de production gr\u00e2ce \u00e0 des donn\u00e9es de tests particuli\u00e8res afin de valider le comportement des mod\u00e8les.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ff09a0e elementor-widget elementor-widget-image\" data-id=\"ff09a0e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1289\" height=\"504\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/06\/image3-4.png\" class=\"attachment-full size-full wp-image-203700\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1eb60e4 e-flex e-con-boxed e-con\" data-id=\"1eb60e4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2cf60ff elementor-widget elementor-widget-heading\" data-id=\"2cf60ff\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bf5cc81 elementor-widget elementor-widget-text-editor\" data-id=\"bf5cc81\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Amazon SageMaker est une plateforme particuli\u00e8rement utile et compl\u00e8te pour les data scientists gr\u00e2ce \u00e0 la vari\u00e9t\u00e9, la qualit\u00e9, et l\u2019int\u00e9r\u00eat des outils qu\u2019elle poss\u00e8de. En effet, ces outils peuvent \u00eatre utilis\u00e9s pour l\u2019int\u00e9gralit\u00e9 des phases de d\u00e9veloppement d\u2019un mod\u00e8le de Machine Learning comme la pr\u00e9paration des donn\u00e9es ainsi que l\u2019entra\u00eenement et le d\u00e9ploiement du mod\u00e8le, jusqu\u2019\u00e0 la maintenance du mod\u00e8le d\u00e9ploy\u00e9. Ainsi, la plateforme AWS SageMaker est destin\u00e9e \u00e0 \u00eatre utilis\u00e9e plus fr\u00e9quemment par les Data Scientists pour faire du Machine Learning en raison de ses nombreuses fonctionnalit\u00e9s centralis\u00e9es.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e28dded elementor-widget elementor-widget-text-editor\" data-id=\"e28dded\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Si vous souhaitez vous former \u00e0 la Data Science pour creuser plus en profondeur les sujets relatifs au Machine Learning, n\u2019h\u00e9sitez pas \u00e0 consulter nos formations de <a href=\"https:\/\/datascientest.com\/formation-machine-learning-engineer\">Machine Learning Engineering<\/a><\/span><span style=\"font-weight: 400;\">et de Data Science<\/span><span style=\"font-weight: 400;\">qui peuvent \u00e9galement vous permettre d\u2019obtenir la <a href=\"https:\/\/datascientest.com\/formation-aws\">certification AWS<\/a>.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-624c28d elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"624c28d\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-aws\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Commencer une formation sur Amazon<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2e46b1b e-flex e-con-boxed e-con\" data-id=\"2e46b1b\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-764b668 elementor-widget elementor-widget-html\" data-id=\"764b668\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Les fonctionnalit\u00e9s principales de SageMaker\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Tout d\u2019abord, pour utiliser la plateforme SageMaker, l\u2019utilisateur dispose de deux possibilit\u00e9s : Utiliser une instance de notebook \u00e0 travers l\u2019environnement SageMaker Notebooks Instances pour ex\u00e9cuter des notebooks Jupyter. 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Cela permet ainsi de r\u00e9duire les co\u00fbts li\u00e9s \u00e0 la labellisation des donn\u00e9es.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Amazon SageMaker est une plateforme cloud con\u00e7ue par Amazon Web Services (AWS) particuli\u00e8rement utile pour la Data Science. D\u00e9couvrez ci-dessous plus de d\u00e9tails sur les fonctionnalit\u00e9s principales ainsi que l&rsquo;utilisation des outils de la plateforme en allant du d\u00e9veloppement d\u2019un mod\u00e8le de Machine Learning jusqu\u2019\u00e0 son d\u00e9ploiement. Introduction Amazon SageMaker est une plateforme cloud bas\u00e9e [&hellip;]<\/p>\n","protected":false},"author":79,"featured_media":203660,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2411],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AWS SageMaker : Guide d&#039;utilisation de la plateforme<\/title>\n<meta name=\"description\" content=\"Amazon SageMaker est une plateforme cloud con\u00e7ue par Amazon Web Services (AWS) particuli\u00e8rement utile pour la Data Science. 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