Developing and Deploying AI/ML Applications on Red Hat OpenShift AI
Official partner
Red Hat
Course Description
Operationalize the complete life cycle of modern AI applications at scale by using Red Hat OpenShift AI.
Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) provides students with the fundamental knowledge to manage the complete life cycle of modern AI applications. This course helps students build core skills for using Red Hat OpenShift AI to efficiently train, test, deploy, and monitor both predictive and generative AI models at scale.
This course is based on Red Hat OpenShift ® 4.20, and Red Hat OpenShift AI 3.3.
Course Summary
Introduction to Red Hat OpenShift AI
Using Workbenches for AI/ML Development
Fundamentals of Model Serving
Serving Predictive AI Models
Monitoring AI Models
Introduction to AI Pipelines
Advanced AI Pipelines Development and ExperimentsGen AI Model Optimization and Evaluation
Building GenAI Applications
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