AI267 – Developing and Deploying AI/ML Applications on Red Hat OpenShift AI
Operationalize the complete life cycle of modern AI applications at scale by using 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.
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
Prerequisites
- 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
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.
Outline
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.
Use workbench environments for AI/ML development and connect them to data sources and stores.
Prepare, deploy, and serve models by using OpenShift AI model serving capabilities.
Deploy and serve AI models with specific runtimes, including OpenVINO for predictive models and vLLM for large language 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.
Create and manage basic data science pipelines by using Elyra and Kubeflow SDK to automate fundamental AI/ML workflows.
Implement advanced pipeline features including container components, artifacts management, Kubernetes configuration, and systematic experimentation for production MLOps workflows.
Systematically select, optimize, and evaluate large language models by using RHOAI's model catalog, compression techniques, and evaluation frameworks.
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.
