Module 1: Introduction to Red Hat OpenShift AI
Identify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat AI.
Module 2: Data Science Projects
Organize code and configuration by using data science projects, workbenches, and data connections
Module 3: Jupyter Notebooks
Use Jupyter notebooks to execute and test code interactively
Module 4: Red Hat OpenShift AI Installation
Install Red Hat OpenShift AI and manage Red Hat OpenShift AI components
Module 5: User and Resource Management
Manage Red Hat OpenShift AI users and allocate resources
Module 6: Custom Notebook Images
Create and import custom notebook images in Red Hat OpenShift AI
Module 7: Introduction to Machine Learning
Describe basic machine learning concepts, different types of machine learning, and machine learning workflows
Module 8: Training Models
Train models by using default and custom workbenches
Module 9: Enhancing Model Training with RHOAI
Use RHOAI to apply best practices in machine learning and data science
Module 10: Introduction to Model Serving
Describe the concepts and components required to export, share and serve trained machine learning models
Module 11: Model Serving in Red Hat OpenShift AI
Serve trained machine learning models with OpenShift AI
Module 12: Introduction to Data Science Pipelines
Define and set up Data Science Pipelines
Module 13: Working with Pipelines
Create data science pipelines with the Kubeflow SDK and Elyra
Module 14: Controlling Pipelines and Experiments
Configure, monitor, and track pipelines with artifacts, metrics, and experiments