Course Description
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
Target Audience
- 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
Recommended training
- 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