Kubeflow Pipelines<\/li><\/ul>","outline":"Introduction to Red Hat OpenShift AI<\/h5>
Identify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat AI<\/p>\n
Data Science Projects<\/h5>
Organize code and configuration by using data science projects, workbenches, and data connections<\/p>\n
Jupyter Notebooks<\/h5>
Use Jupyter notebooks to execute and test code interactively<\/p>\n
Installing Red Hat OpenShift AI<\/h5>
Installing Red Hat OpenShift AI by using the web console and the CLI, and managing Red Hat OpenShift AI components<\/p>\n
Managing Users and Resources<\/h5>
Managing Red Hat OpenShift AI users, and resource allocation for Workbenches<\/p>\n
Custom Notebook Images<\/h5>
Creating custom notebook images, and importing a custom notebook through the Red Hat OpenShift AI dashboard<\/p>\n
Introduction to Machine Learning<\/h5>
Describe basic machine learning concepts, different types of machine learning, and machine learning workflows<\/p>\n
Training Models<\/h5>
Train models by using default and custom workbenches<\/p>\n
Enhancing Model Training with RHOAI<\/h5>
Use RHOAI to apply best practices in machine learning and data science<\/p>\n
Introduction to Model Serving<\/h5>
Describe the concepts and components required to export, share and serve trained machine learning models<\/p>\n
Model Serving in Red Hat OpenShift AI<\/h5>
Serve trained machine learning models with OpenShift AI<\/p>\n
Introduction to Data Science Pipelines<\/h5>
Create, run, manage, and troubleshoot data science pipelines<\/p>\n
Elyra Pipelines<\/h5>
Create data science pipelines with Elyra<\/p>\n
Kubeflow Pipelines<\/h5>
Create data science pipelines with Kubeflow Pipelines<\/p>","summary":"
Une introduction au développement et au déploiement d’applications AI\/ML sur Red Hat OpenShift AI.<\/p>\n
Developing and Deploying AI\/ML Applications on Red Hat OpenShift AI (AI267) fournit aux étudiants les connaissances fondamentales sur l’utilisation de Red Hat OpenShift pour développer et déployer des applications AI\/ML. Ce cours aide les étudiants à acquérir des compétences essentielles pour utiliser Red Hat OpenShift AI afin d’entraîner, développer et déployer des modèles de machine learning à travers des exercices pratiques.<\/p>\n
Ce cours se base sur Red Hat OpenShift® 4.14 et Red Hat OpenShift AI 2.8. Le !<\/span> est inclus dans l’offre.<\/p>","objective_plain":"Impact on the Organization\n\nLes organisations collectent et stockent d\u2019\u00e9normes quantit\u00e9s d\u2019informations provenant de multiples sources. Avec Red Hat OpenShift AI, elles disposent d\u2019une plateforme pr\u00eate \u00e0 analyser les donn\u00e9es, visualiser les tendances et patterns, et pr\u00e9dire les r\u00e9sultats futurs gr\u00e2ce aux algorithmes de machine learning et d\u2019intelligence artificielle.\n\n[h5Impact on the Individual[\/h5]\n\n\u00c0 l\u2019issue de ce cours, vous comprendrez les fondations de l\u2019architecture Red Hat OpenShift AI. Vous serez capable d\u2019installer Red Hat OpenShift AI, de g\u00e9rer les allocations de ressources, de mettre \u00e0 jour les composants et de g\u00e9rer les utilisateurs ainsi que leurs permissions. Vous serez \u00e9galement capable d\u2019entra\u00eener, de d\u00e9ployer et de servir des mod\u00e8les, y compris d\u2019utiliser Red Hat OpenShift AI pour appliquer les bonnes pratiques en machine learning et data science. Enfin, vous serez capable de cr\u00e9er, ex\u00e9cuter, g\u00e9rer et d\u00e9panner des data science pipelines.","essentials_plain":"- Une exp\u00e9rience avec Git est requise\n- Une exp\u00e9rience en d\u00e9veloppement Python est requise, ou avoir suivi le cours Python Programming with Red Hat (AD141)\n- Une exp\u00e9rience avec Red Hat OpenShift est requise, ou avoir suivi le cours Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288)\n- Une exp\u00e9rience de base dans les domaines AI, data science et machine learning est recommand\u00e9e","audience_plain":"- Data scientists et praticiens de l\u2019IA qui veulent utiliser Red Hat OpenShift AI pour construire et entra\u00eener des mod\u00e8les ML\n- D\u00e9veloppeurs qui veulent cr\u00e9er et int\u00e9grer des applications AI\/ML\n- MLOps engineers responsables de l\u2019installation, de la configuration, du d\u00e9ploiement et de la supervision des applications AI\/ML sur Red Hat OpenShift AI","contents_plain":"- Introduction \u00e0 Red Hat OpenShift AI\n- Projets de data science\n- Jupyter Notebooks\n- Installation de Red Hat OpenShift AI\n- Gestion des utilisateurs et des ressources\n- Images personnalis\u00e9es de notebooks\n- Introduction au machine learning\n- Entra\u00eenement de mod\u00e8les\n- Am\u00e9lioration de l\u2019entra\u00eenement de mod\u00e8les avec RHOAI\n- Introduction au Model Serving\n- Model Serving dans Red Hat OpenShift AI\n- Introduction \u00e0 l\u2019automatisation des workflows\n- Elyra Pipelines\n- Kubeflow Pipelines","outline_plain":"Introduction to Red Hat OpenShift AI\n\nIdentify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat AI\n\nData Science Projects\n\nOrganize code and configuration by using data science projects, workbenches, and data connections\n\nJupyter Notebooks\n\nUse Jupyter notebooks to execute and test code interactively\n\nInstalling Red Hat OpenShift AI\n\nInstalling Red Hat OpenShift AI by using the web console and the CLI, and managing Red Hat OpenShift AI components\n\nManaging Users and Resources\n\nManaging Red Hat OpenShift AI users, and resource allocation for Workbenches\n\nCustom Notebook Images\n\nCreating custom notebook images, and importing a custom notebook through the Red Hat OpenShift AI dashboard\n\nIntroduction to Machine Learning\n\nDescribe basic machine learning concepts, different types of machine learning, and machine learning workflows\n\nTraining Models\n\nTrain models by using default and custom workbenches\n\nEnhancing Model Training with RHOAI\n\nUse RHOAI to apply best practices in machine learning and data science\n\nIntroduction to Model Serving\n\nDescribe the concepts and components required to export, share and serve trained machine learning models\n\nModel Serving in Red Hat OpenShift AI\n\nServe trained machine learning models with OpenShift AI\n\nIntroduction to Data Science Pipelines\n\nCreate, run, manage, and troubleshoot data science pipelines\n\nElyra Pipelines\n\nCreate data science pipelines with Elyra\n\nKubeflow Pipelines\n\nCreate data science pipelines with Kubeflow Pipelines","summary_plain":"Une introduction au d\u00e9veloppement et au d\u00e9ploiement d\u2019applications AI\/ML sur Red Hat OpenShift AI.\n\nDeveloping and Deploying AI\/ML Applications on Red Hat OpenShift AI (AI267) fournit aux \u00e9tudiants les connaissances fondamentales sur l\u2019utilisation de Red Hat OpenShift pour d\u00e9velopper et d\u00e9ployer des applications AI\/ML. Ce cours aide les \u00e9tudiants \u00e0 acqu\u00e9rir des comp\u00e9tences essentielles pour utiliser Red Hat OpenShift AI afin d\u2019entra\u00eener, d\u00e9velopper et d\u00e9ployer des mod\u00e8les de machine learning \u00e0 travers des exercices pratiques.\n\nCe cours se base sur Red Hat OpenShift\u00ae 4.14 et Red Hat OpenShift AI 2.8. Le (!) est inclus dans l\u2019offre.","skill_level":"Intermediate","version":"1.0","duration":{"unit":"d","value":3,"formatted":"3 jours"},"pricelist":{"List Price":{"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":3168},"SE":{"country":"SE","currency":"EUR","taxrate":25,"price":3168},"IT":{"country":"IT","currency":"EUR","taxrate":20,"price":2541},"AE":{"country":"AE","currency":"USD","taxrate":5,"price":2641},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":3168},"GB":{"country":"GB","currency":"GBP","taxrate":20,"price":2741},"PL":{"country":"PL","currency":"EUR","taxrate":23,"price":2237},"SI":{"country":"SI","currency":"EUR","taxrate":20,"price":3168},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":3168},"FR":{"country":"FR","currency":"EUR","taxrate":19.6,"price":3168}}},"lastchanged":"2026-03-16T18:08:47+01:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course-schedule\/34856","source_lang":"fr","source":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course\/redhat-ai268"}}