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Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267)
Code:
AI267
Duration:
1 Session
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$3525
USD
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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 (AI267) provides students with the fundamental knowledge about using Red Hat OpenShift for developing and deploying AI/ML applications. This course helps students build core skills for using Red Hat OpenShift AI to train, develop and deploy machine learning models through hands-on experience.
This course is based on Red Hat OpenShift 4.14, and Red Hat OpenShift AI 2.8.
Note: This course is offered as a 3 day in person class, a 4 day virtual class or is self-paced. Durations may vary based on the delivery. For full course details, scheduling, and pricing, select your location then get started on the right hand menu.
Course Content Summary
- Introduction to Red Hat OpenShift AI
- Data Science Projects
- Jupyter Notebooks
- Installing Red Hat OpenShift AI
- Managing Users and Resources
- 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 Workflow Automation
- Elyra Pipelines
- KubeFlow Pipelines
Red Hat Learning Subscription COURSE Included with Your Purchase
Starting January 1, 2026, Red Hat introduces RHLS-Course a flexible subscription model now included with this catalog offering. This replaces the previous direct virtual class enrollment from Global Knowledge.
When you purchase this item, youll receive an RHLS subscription at the course level, giving you the freedom to choose the schedule that works best and self-enroll in your selected class.
Your RHLS subscription includes:
- One live, instructor-led virtual session
- 12 months of self-paced learning access
- One certification exam with a free retake
Onsite Classroom-based sessions and closed course options remain unchanged.
This course is available in the following formats:
Duration: 1 Session
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Delivery Format: Virtual Classroom
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$ 3525 |
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Delivery Format: Virtual Classroom
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$ 3525 |
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Delivery Format: Virtual Classroom
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$ 3525 |
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Delivery Format: Virtual Classroom
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$ 3525 |
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Delivery Format: Virtual Classroom
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$ 3525 |
Impact on the Organization
- Organizations collect and store vast amounts of information from multiple sources. With Red Hat OpenShift AI, organizations have a platform ready to analyze data, visualize trends and patterns, and predict future business outcomes by using machine learning and artificial intelligence algorithms.
Impact on the Individual
- As a result of attending this course, you will understand the foundations of the Red Hat OpenShift AI architecture. You will be able to install Red Hat OpenShift AI, manage resource allocations, update components and manage users and their permissions. You will also be able to train, deploy and serve models, including how to use Red Hat OpenShift AI to apply best practices in machine learning and data science. Finally you will be able to create, run, manage and troubleshoot data science pipelines.
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Will Be Updated Soon!
Module 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.
Module 2:Data Science Projects
- Organize code and configuration by using data science projects, workbenches, and data connections
Module 3:Jupyter Notebooks
- Use Jupyter notebooks to execute and test code interactively
Module 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
Module 5:Managing Users and Resources
- Managing Red Hat OpenShift AI users, and resource allocation for Workbenches
Module 6:Custom Notebook Images
- Creating custom notebook images, and importing a custom notebook through the Red Hat OpenShift AI dashboard
Module 7:Introduction to Machine Learning
- Describe basic machine learning concepts, different types of machine learning, and machine learning workflows
Module 8:Training Models
- Train models by using default and custom workbenches
Module 9:Enhancing Model Training with RHOAI
- Use RHOAI to apply best practices in machine learning and data science
Module 10:Introduction to Model Serving
- Describe the concepts and components required to export, share and serve trained machine learning modelsI
Module 11:Model Serving in Red Hat OpenShift AI
- Serve trained machine learning models with OpenShift AI
Module 12:Custom Model Servers
- Deploy and serve machine learning models by using custom model serving runtimes
Module 13:Introduction to Data Science Pipelines
- Create, run, manage, and troubleshoot data science pipelines
Module 14:Elyra Pipelines
- Creating a Data Science Pipeline with Elyra
Module 15:KubeFlow Pipelines
- Creating a Data Science Pipeline with KubeFlow SDK
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Will Be Updated Soon!
- Data scientists and AI practitioners who want to use Red Hat OpenShift AI to build and train ML models
- Developers who want to build and integrate AI/ML enabled applications
- MLOps engineers responsible for installing, configuring, deploying, and monitoring AI/ML applications on Red Hat OpenShift AI
- Experience with Git is required
- Experience in Python development is required, or completion of the Python Programming with Red Hat (AD141) course
- Experience in Red Hat OpenShift is required, or completion of the Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288) course
- Basic experience in the AI, data science, and machine learning fields is recommended
Technology considerations
- No ILT classroom will be available