Detailed Course Outline
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