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