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
Installing Red Hat OpenShift AI
Installing Red Hat
OpenShift AI by using the web console and the CLI, and managing Red Hat
OpenShift AI components
Managing Users and Resources
Managing Red Hat
OpenShift AI users, and resource allocation for Workbenches
Custom Notebook Images
Creating custom notebook
images, and importing a custom notebook through the Red Hat OpenShift AI
dashboard
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
Create, run,
manage, and troubleshoot data science pipelines
Elyra Pipelines
Create data science pipelines with
Elyra
Kubeflow Pipelines
Create data science pipelines with
Kubeflow Pipelines
