Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267)

Agenda

1. 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.

2. Data Science Projects

• Organize code and configuration by using data science projects, workbenches, and data connections

3. Jupyter Notebooks

• Use Jupyter notebooks to execute and test code interactively

4. 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

5. Managing Users and Resources

• Managing Red Hat OpenShift AI users, and resource allocation for Workbenches

6. Custom Notebook Images

• Creating custom notebook images, and importing a custom notebook through the Red Hat OpenShift AI dashboard

7. Introduction to Machine Learning

• Describe basic machine learning concepts, different types of machine learning, and machine learning workflows

8. Training Models

• Train models by using default and custom workbenches

9. Enhancing Model Training with RHOAI

• Use RHOAI to apply best practices in machine learning and data science

10. Introduction to Model Serving

• Describe the concepts and components required to export, share and serve trained machine learning models

11. Model Serving in Red Hat OpenShift AI

• Serve trained machine learning models with OpenShift AI

12. Custom Model Servers

• Deploy and serve machine learning models by using custom model serving runtimes

13. Introduction to Workflow Automation

• Create, run, manage, and troubleshoot data science pipelines

14. Elyra Pipelines

• Creating a Data Science Pipeline with Elyra

  15. KubeFlow Pipelines

  • Creating a Data Science Pipeline with KubeFlow SDK