Curriculum
- 1 Section
- 14 Lessons
- 4 Days
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- Topics14
- 1.1Introduction to Red Hat OpenShift AI Identify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat AI
- 1.2Data Science Projects Organize code and configuration by using data science projects, workbenches, and data connections
- 1.3Jupyter Notebooks Use Jupyter notebooks to execute and test code interactively
- 1.4Installing Red Hat OpenShift AI Installing Red Hat OpenShift AI by using the web console and the CLI, and managing Red Hat OpenShift AI components
- 1.5Managing Users and Resources Managing Red Hat OpenShift AI users, and resource allocation for Workbenches
- 1.6Custom Notebook Images Creating custom notebook images, and importing a custom notebook through the Red Hat OpenShift AI dashboard
- 1.7Introduction to Machine Learning Describe basic machine learning concepts, different types of machine learning, and machine learning workflows
- 1.8Training Models Train models by using default and custom workbenches
- 1.9Enhancing Model Training with RHOAI Use RHOAI to apply best practices in machine learning and data science
- 1.10Introduction to Model Serving Describe the concepts and components required to export, share and serve trained machine learning models
- 1.11Model Serving in Red Hat OpenShift AI Serve trained machine learning models with OpenShift AI
- 1.12Introduction to Data Science Pipelines Create, run, manage, and troubleshoot data science pipelines
- 1.13Elyra Pipelines Create data science pipelines with Elyra
- 1.14Kubeflow Pipelines Create data science pipelines with Kubeflow Pipelines