MLOps engineers responsables de l’opérationnalisation du cycle de vie ML sur Red Hat OpenShift AI<\/li><\/ul>","contents":"Course Content Summary<\/strong><\/p>\n\n- Introduction to Red Hat OpenShift AI<\/li>
- Data Science Projects<\/li>
- Jupyter Notebooks<\/li>
- Red Hat OpenShift AI Installation<\/li>
- Users and Resources Management<\/li>
- Custom Notebook Images<\/li>
- Introduction to Machine Learning<\/li>
- Training Models<\/li>
- Enhancing Model Training with RHOAI<\/li>
- Introduction to Model Serving<\/li>
- Model Serving in Red Hat OpenShift AI<\/li>
- Introduction to Data Science Pipelines<\/li>
- Working with Pipelines<\/li>
- Controlling Pipelines and Experiments<\/li><\/ul>","outline":"
Introduction to Red Hat OpenShift AI<\/strong>\n<\/p>\n\n- Identify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat AI.<\/li><\/ul>
Data Science Projects<\/strong>\n<\/p>\n\n- Organize code and configuration by using data science projects, workbenches, and data connections<\/li><\/ul>
Jupyter Notebooks<\/strong>\n<\/p>\n\n- Use Jupyter notebooks to execute and test code interactively<\/li><\/ul>
Red Hat OpenShift AI Installation<\/strong>\n<\/p>\n\n- Install Red Hat OpenShift AI and manage Red Hat OpenShift AI components<\/li><\/ul>
User and Resource Managemen<\/strong>t\n<\/p>\n\n- Manage Red Hat OpenShift AI users and allocate resources<\/li><\/ul>
Custom Notebook Images<\/strong>\n<\/p>\n\n- Create and import custom notebook images in Red Hat OpenShift AI<\/li><\/ul>
Introduction to Machine Learning<\/strong>\n<\/p>\n\n- Describe basic machine learning concepts, different types of machine learning, and machine learning workflows<\/li><\/ul>
Training Models<\/strong>\n<\/p>\n\n- Train models by using default and custom workbenches<\/li><\/ul>
Enhancing Model Training with RHOAI<\/strong>\n<\/p>\n\n- Use RHOAI to apply best practices in machine learning and data science<\/li><\/ul>
Introduction to Model Serving<\/strong>\n<\/p>\n\n- Describe the concepts and components required to export, share and serve trained machine learning models<\/li><\/ul>
Model Serving in Red Hat OpenShift AI<\/strong>\n<\/p>\n\n- Serve trained machine learning models with OpenShift AI<\/li><\/ul>
Introduction to Data Science Pipelines<\/strong>\n<\/p>\n\n- Define and set up Data Science Pipelines<\/li><\/ul>
Working with Pipelines<\/strong>\n<\/p>\n\n- Create data science pipelines with the Kubeflow SDK and Elyra<\/li><\/ul>
Controlling Pipelines and Experiments<\/strong>\n<\/p>\n\n- Configure, monitor, and track pipelines with artifacts, metrics, and experiments<\/li><\/ul>","summary":"
Une introduction au développement et au déploiement d’applications AI\/ML sur Red Hat OpenShift AI.<\/strong><\/p>\nDeveloping 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.<\/p>","objective_plain":"[bImpact on the Organization[\/b]\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\nImpact on the Individual\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, d\u00e9ployer et servir des mod\u00e8les, y compris comment utiliser Red Hat OpenShift AI pour appliquer les bonnes pratiques en machine learning et data science. Enfin, vous serez capable de d\u00e9finir et configurer des data science pipelines avec Red Hat OpenShift AI.","essentials_plain":"- Une exp\u00e9rience avec Git est requise\n- Une exp\u00e9rience en d\u00e9veloppement Python est requise, ou avoir suivi le cours Red Hat Training Presents: Introduction to Python Programming (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- Basic experience in the AI, data science, and machine learning fields is recommended","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- D\u00e9veloppeurs, data scientists et praticiens de l\u2019IA qui veulent automatiser leurs workflows ML\n- MLOps engineers responsables de l\u2019op\u00e9rationnalisation du cycle de vie ML sur Red Hat OpenShift AI","contents_plain":"Course Content Summary\n\n\n- Introduction to Red Hat OpenShift AI\n- Data Science Projects\n- Jupyter Notebooks\n- Red Hat OpenShift AI Installation\n- Users and Resources Management\n- Custom Notebook Images\n- Introduction to Machine Learning\n- Training Models\n- Enhancing Model Training with RHOAI\n- Introduction to Model Serving\n- Model Serving in Red Hat OpenShift AI\n- Introduction to Data Science Pipelines\n- Working with Pipelines\n- Controlling Pipelines and Experiments","outline_plain":"Introduction to Red Hat OpenShift AI\n\n\n\n\n- Identify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat AI.\nData Science Projects\n\n\n\n\n- Organize code and configuration by using data science projects, workbenches, and data connections\nJupyter Notebooks\n\n\n\n\n- Use Jupyter notebooks to execute and test code interactively\nRed Hat OpenShift AI Installation\n\n\n\n- Install Red Hat OpenShift AI and manage Red Hat OpenShift AI components\nUser and Resource Management\n\n\n\n- Manage Red Hat OpenShift AI users and allocate resources\nCustom Notebook Images\n\n\n\n\n- Create and import custom notebook images in Red Hat OpenShift AI\nIntroduction to Machine Learning\n\n\n\n\n- Describe basic machine learning concepts, different types of machine learning, and machine learning workflows\nTraining Models\n\n\n\n\n- Train models by using default and custom workbenches\nEnhancing Model Training with RHOAI\n\n\n\n\n- Use RHOAI to apply best practices in machine learning and data science\nIntroduction to Model Serving\n\n\n\n\n- Describe the concepts and components required to export, share and serve trained machine learning models\nModel Serving in Red Hat OpenShift AI\n\n\n\n\n- Serve trained machine learning models with OpenShift AI\nIntroduction to Data Science Pipelines\n\n\n\n- Define and set up Data Science Pipelines\nWorking with Pipelines\n\n\n\n- Create data science pipelines with the Kubeflow SDK and Elyra\nControlling Pipelines and Experiments\n\n\n\n- Configure, monitor, and track pipelines with artifacts, metrics, and experiments","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.","skill_level":"Intermediate","version":"1","duration":{"unit":"d","value":3,"formatted":"3 jours"},"pricelist":{"List Price":{"US":{"country":"US","currency":"USD","taxrate":null,"price":3525},"SE":{"country":"SE","currency":"EUR","taxrate":25,"price":2805},"AE":{"country":"AE","currency":"USD","taxrate":5,"price":2280},"IT":{"country":"IT","currency":"EUR","taxrate":20,"price":2175},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":2805},"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":2805},"GB":{"country":"GB","currency":"GBP","taxrate":20,"price":2385},"PL":{"country":"PL","currency":"EUR","taxrate":23,"price":1905},"SI":{"country":"SI","currency":"EUR","taxrate":20,"price":2805},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":2805},"FR":{"country":"FR","currency":"EUR","taxrate":19.6,"price":2805}}},"lastchanged":"2026-03-11T16:27:41+01:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course-schedule\/34590","source_lang":"fr","source":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course\/redhat-ai267"}}