Developing and Deploying AI/ML Applications on Red Hat OpenShift
Al
Developing and Deploying AI/ML Applications on Red Hat OpenShift
Al
The course Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) equips students with essential knowledge for utilizing Red Hat OpenShift in the development and deployment of AI/ML applications. Through hands-on experience, students gain foundational skills to train, develop, and deploy machine learning models using Red Hat OpenShift AI.
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About the course
The course begins with an introduction to Red Hat OpenShift AI, covering its key features, architecture, and components. Students learn to organize code and configurations using data science projects, workbenches, and data connections, and gain hands-on experience with Jupyter notebooks for interactive code execution. The curriculum includes installing and managing Red Hat OpenShift AI using both the web console and CLI, as well as handling user management and resource allocation. It explores creating custom notebook images, importing them through the OpenShift AI dashboard, and introduces fundamental machine learning concepts, workflows, and model training. Advanced topics include enhancing training with RHOAI, serving trained models using OpenShift AI, and deploying custom model servers. The course concludes with creating, managing, and troubleshooting data science pipelines using Elyra and KubeFlow SDK.
Course Content
Introduction to Red Hat OpenShift Al
- Introduction to Red Hat OpenShift Al
- Architecture
Data Science Projects
- Data Science Projects
- Lab: Data Science Projects
- Workbenches
- Lab: Workbenches
- Data Connections
- Lab: Data Connections
Jupyter Notebooks
- Introduction to Jupyter Notebooks
- Lab: Introduction to Jupyter Notebooks
- Collaboration with Jupyter Notebooks
- Lab: Collaboration with Jupyter Notebooks
Installing Red Hat OpenShift Al
- Red Hat OpenShift Al Installation
- Lab: Red Hat OpenShift Al Installation
Manage Users and Resources
- Users, Projects, and Permissions
- Lab: Users, Projects, and Permissions
- Managing Resources
- Lab: Managing Resources
Custom Notebook Images
- Create and Import a Custom Notebook Image
- Lab: Create and Import a Custom Notebook Image
Introduction to Machine Learning
- Machine Learning Concepts
- Machine Learning Workflow
Training Models
- Training ML Models
- Lab: Training ML Models
- Training Models with Custom Workbenches
- Lab: Training Models with Custom Workbenches
Enhancing Model Training with RHOAI
- Inspecting Workbench Resources
- Lab: Inspecting Workbench Resources
- Scaling Data Loading
- Lab: Scaling Data Loading
- Monitoring the Training Process
- Lab: Monitoring the Training Process
- Software Engineering Principles for Data Science
- Lab: Software Engineering Principles for Data Science
Introduction to Model Serving
- Concepts and Key Aspects of Serving Al Models
- Saving Trained Machine Learning Models
- Lab: Saving Trained Machine Learning Models
- Serving Models as Stand-Alone Applications
- Lab: Serving Models as Stand-Alone Applications
Model Serving in Red Hat OpenShift Al
- Using Model Servers to Deploy Models
- Lab: Using Model Servers to Deploy Models
- Consuming the Model Serving API
- Lab: Consuming the Model Serving API
- Creating and Using Model Servers
- Lab: Creating and Using Model Servers
Introduction to Data Science Pipelines
- Concepts of Data Science Pipelines
- Creating a Pipeline Server
- Lab: Creating a Pipeline Server
Elyra Pipelines
- Creating Pipelines with Elyra
- Lab: Creating Pipelines with Elyra
Kubeflow Pipelines
- Creating Pipelines with Kubeflow Pipelines
- Lab: Creating Pipelines with Kubeflow Pipelines
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