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

Overview

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.

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 Kubeflow Pipelines Development and Experiments
  • Gen AI Model Optimization and Evaluation
  • Building GenAI Applications

Pre-Requisites

  • Experience with Git is required
  • Experience in Python development will help
  • 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

Target Audience

  • 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
  • MLOps engineers responsible for installing, configuring, deploying, and monitoring AI/ML applications on Red Hat OpenShift AI

Duration: 3 days (Full-time)

Training Fee: Call or email for best offer

Course Outline

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.
Using Workbenches for AI/ML Development
Use workbench environments for AI/ML development and connect them to data sources and stores.
Fundamentals of Model Serving
Prepare, deploy, and serve models by using OpenShift AI model serving capabilities.
Serving Predictive AI Models
Deploy and serve predictive AI models with specific runtimes, including OpenVINO.components
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.
Introduction to AI Pipelines
Create and manage basic data science pipelines by using Elyra and Kubeflow SDK to automate fundamental AI/ML workflows.
Advanced AI Pipelines Development and Experiments
Implement advanced pipeline features including container components, artifacts management, Kubernetes configuration, and systematic experimentation for production MLOps workflows.
Gen AI Model Optimization and Evaluation
Systematically optimize and evaluate large language models by using RHOAI’s compression techniques and evaluation frameworks.
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.
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