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

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.

Course Outline

1. Introduction to Red Hat OpenShift AI

• Identify how Red Hat OpenShift AI provides a complete MLOps and GenAIOps platform and how to use it to configure data science projects for team collaboration.

2. Using Workbenches for AI/ML Development

• Use workbench environments for AI/ML development and connect them to data sources and stores.

3.Fundamentals of Model Serving

• Prepare, deploy, and serve models by using OpenShift AI model serving capabilities.

4. Serving Predictive AI Models

• Deploy and serve predictive AI models with specific runtimes, including OpenVINO.

5. Monitoring AI Models

• Monitor deployed models for bias, data drift, and performance by using TrustyAI and observability tools to ensure reliable and ethical AI performance in production.

6. Introduction to AI Pipelines

• Create and manage basic data science pipelines by using Elyra and Kubeflow SDK to automate fundamental AI/ML workflows.

7. Advanced AI Pipelines Development and Experiments

• Implement advanced pipeline features including container components, artifacts management, Kubernetes configuration, and systematic experimentation for production MLOps workflows.

8. Gen AI Model Optimization and Evaluation

• Systematically optimize and evaluate large language models by using RHOAI’s compression techniques and evaluation frameworks.

9. Building GenAI Applications

• Build production-ready GenAI applications by using industry patterns including RAG, agentic workflows, and trustworthy AI practices, and move beyond basic model serving to ship complete intelligent solutions.