1. Introduction to Red Hat OpenShift AI
Identify OpenShift AI’s main features, components, and overall architecture.
2. Data Science Projects
Organize and manage project code, configurations, and data connections using workbenches.
3. Jupyter Notebooks
Execute, visualize, and test code interactively through Jupyter environments.
4. Installing Red Hat OpenShift AI
Install and configure OpenShift AI components for AI/ML workloads.
5. User and Resource Management
Administer user access and control compute resource allocations.
6. Custom Notebook Images
Create, customize, and import notebook images for specialized workloads.
7. Introduction to Machine Learning
Learn ML fundamentals, key algorithms, and workflow design.
8. Training Models
Train models using standard or custom workbenches within RHOAI.
9. Enhancing Model Training with RHOAI
Implement best practices in ML and data science using Red Hat OpenShift AI tools.
10. Introduction to Model Serving
Explore the principles and components needed to serve trained models.
11. Model Serving in OpenShift AI
Deploy and manage production-ready models in OpenShift AI environments.
12. Data Science Pipelines
Set up data pipelines for end-to-end automation and reproducibility.
13. Working with Pipelines
Build pipelines using Kubeflow SDK and Elyra.
14. Controlling Pipelines and Experiments
Track metrics, artifacts, and experiments for ML lifecycle management.
