Developing and Deploying AI/ML Applications on Red Hat OpenShift AI
Official partner
Red Hat
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 ® 4.16, and Red Hat OpenShift AI 2.13.
Course Summary
Introduction to Red Hat OpenShift AI
Data Science Projects
Jupyter Notebooks
Red Hat OpenShift AI Installation
User and Resource 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
Prerequisites for this course
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
Audience for this course
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
Developers, data scientists, and AI practitioners who want to automate their ML workflows
MLOps engineers responsible for operationalizing the ML lifecycle on Red Hat OpenShift AI