Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI268)
Developing and Deploying AI/ML Applications on Red Hat OpenShift AI teaches students to build, train, and deploy machine learning models efficiently. With hands-on training, they’ll learn to manage AI/ML workloads and automate workflows using OpenShift AI 2.13 on Red Hat OpenShift 4.16. The course includes the Red Hat Certified Specialist in OpenShift AI Exam (EX267).
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AI268
Developing and Deploying AI/ML Applications on Red Hat OpenShift AI with Exam
An introduction to developing and deploying AI/ML applications on Red Hat OpenShift AI.
Developing and Deploying AI/ML Applications on Red Hat OpenShift AI teaches students to build, train, and deploy machine learning models efficiently. With hands-on training, they’ll learn to manage AI/ML workloads and automate workflows using OpenShift AI 2.13 on Red Hat OpenShift 4.16. The course includes the Red Hat Certified Specialist in OpenShift AI Exam (EX267)
Course Content Summary
- Introduction to Red Hat OpenShift AI
- Data Science Projects
- Jupyter Notebooks
- Red Hat OpenShift AI Installation
- User and Resources Management
- Custom Notebook Images
- Introduction to Machine Learning
- Training Models
- Enhancing Model Training with RHOAI
- Introduction to Model Serving
- Model Serving in Red Hat OpenShift AI
- Introduction to Data Science Pipelines
- Working with Pipelines
- Controlling Pipelines and Experiments
Recommended training
Git experience (required)
Python development experience or completion of Python Programming with Red Hat (AD141)
Red Hat OpenShift experience or completion of Red Hat OpenShift Developer II: Building and Deploying Cloud-Native Applications (DO288)
Basic knowledge of AI, data science, and machine learning (recommended)
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
Impact on Your Organization
Organizations generate and store massive amounts of data from various sources. Red Hat OpenShift AI provides a powerful platform to analyze data, uncover trends, and make predictions using machine learning and AI algorithms—helping businesses turn data into actionable insights.
Impact on the Individual
By the end of this course, you’ll have a solid understanding of Red Hat OpenShift AI’s architecture and how to use it effectively. You’ll learn to install and manage OpenShift AI, allocate resources, update components, and control user access. Additionally, you’ll gain hands-on experience in training, deploying, and serving machine learning models, while applying best practices in AI and data science. Finally, you’ll be able to define and set up data science pipelines, streamlining workflows for scalable AI/ML projects.
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