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
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
Custom Model Servers
- Deploy and serve machine learning models by using custom model serving runtimes
Introduction to Workflow Automation
- Create, run, manage, and troubleshoot data science pipelines
Elyra Pipelines
- Creating a Data Science Pipeline with Elyra
KubeFlow Pipelines
- Creating a Data Science Pipeline with KubeFlow SDK