Course subjects
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
Organise 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.