AI267 – Developing and Deploying AI/ML Applications on Red Hat OpenShift AI

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 ^® 4.18, and Red Hat OpenShift AI 2.25.

Course Content Summary

• Introduction to Red Hat OpenShift AI
• Using Workbenches for AI/ML Development
• Fundamentals of Model Serving
• Serving Generative and Predictive AI Models
• Monitoring AI Models
• Introduction to Data Science Pipelines
• Advanced Kubeflow Pipelines Development and Experiments
• GenAI Model Selection, Optimization, and Evaluation
• Building GenAI Applications

Target Audience

• ML Engineers responsible for handling the operational tasks of the MLOps/LLMOps lifecycle, such as deployment, automation, and monitoring.
• Data Scientists who train, deploy, and track their own models. 

Recommended training

• Take our free assessment to gauge whether this offering is the best fit for your skills.
• A basic understanding of machine learning principles and workflows.
• A basic understanding of Generative AI and Large Language Models (LLMs).
• Basic 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