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":"
An introduction to developing and deploying AI\/ML applications on Red Hat OpenShift AI.<\/p>\n
Developing and Deploying AI\/ML Applications on Red Hat OpenShift AI (AI267) provides students with the fundamental knowledge about using Red Hat OpenShift for developing and deploying AI\/ML applications. This course helps students build core skills for using Red Hat OpenShift AI to train, develop and deploy machine learning models through hands-on experience.<\/p>\n
This course is based on Red Hat OpenShift® 4.14, and Red Hat OpenShift AI 2.8. The !<\/span> is included in the offering.<\/p>","objective_plain":"Impact on the Organization\n\nOrganizations collect and store vast amounts of information from multiple sources. With Red Hat OpenShift AI, organizations have a platform ready to analyze data, visualize trends and patterns, and predict future business outcomes by using machine learning and artificial intelligence algorithms.\n\nImpact on the Individual\n\nAs a result of attending this course, you will understand the foundations of the Red Hat OpenShift AI architecture. You will be able to install Red Hat OpenShift AI, manage resource allocations, update components and manage users and their permissions. You will also be able to train, deploy and serve models, including how to use Red Hat OpenShift AI to apply best practices in machine learning and data science. Finally you will be able to create, run, manage and troubleshoot data science pipelines.","essentials_plain":"- Experience with Git is required\n- Experience in Python development is required, or completion of the Python Programming with Red Hat (AD141) course\n- Experience in Red Hat OpenShift is required, or completion of the Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288) course\n- Basic experience in the AI, data science, and machine learning fields is recommended","audience_plain":"- Data scientists and AI practitioners who want to use Red Hat OpenShift AI to build and train ML models\n- Developers who want to build and integrate AI\/ML enabled applications\n- MLOps engineers responsible for installing, configuring, deploying, and monitoring AI\/ML applications on Red Hat OpenShift AI","contents_plain":"- Introduction to Red Hat OpenShift AI\n- Data Science Projects\n- Jupyter Notebooks\n- Installing Red Hat OpenShift AI\n- Managing Users and Resources\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 Workflow Automation\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":"An introduction to developing and deploying AI\/ML applications on Red Hat OpenShift AI.\n\nDeveloping and Deploying AI\/ML Applications on Red Hat OpenShift AI (AI267) provides students with the fundamental knowledge about using Red Hat OpenShift for developing and deploying AI\/ML applications. This course helps students build core skills for using Red Hat OpenShift AI to train, develop and deploy machine learning models through hands-on experience.\n\nThis course is based on Red Hat OpenShift\u00ae 4.14, and Red Hat OpenShift AI 2.8. The (!) is included in the offering.","skill_level":"Intermediate","version":"1.0","duration":{"unit":"d","value":3,"formatted":"3 days"},"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\/en\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/en\/json-course-schedule\/34856","source_lang":"en","source":"https:\/\/portal.flane.ch\/swisscom\/en\/json-course\/redhat-ai268"}}