Developing and Deploying AI/ML Applications on Red Hat OpenShift AI
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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.
What you'll learn
- 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
Course Outline
- 01
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.
- 02
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.
- 03
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.
- 04
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.
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
Who should attend
- ›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.
Outcomes
Impact on the organisation
Organizations often see their data science efforts slowed by manual tasks and the increasing complexity of integrating AI tools, especially with Generative AI. With Red Hat OpenShift AI, organizations gain a unified platform to manage the complete life cycle of modern AI applications. This capability allows them to efficiently train, test, deploy, and monitor both predictive and generative AI models at scale, transforming experimental initiatives into reliable business outcomes.
Impact on the individual
As a result of attending this course, you will be able to manage the complete life cycle of modern AI applications, by efficiently training, testing, deploying, and monitoring both predictive and generative AI models at scale. You will learn to configure collaborative data science projects, efficiently utilize workbench environments, and assign specialized resources. You will prepare, deploy, and serve models by using specialized runtimes. Furthermore, you will automate MLOps workflows by creating advanced data science pipelines, and build production-ready GenAI solutions. Finally, you will ensure reliable and ethical AI performance by monitoring deployed models for bias and data drift using, and implement safety guardrails for generative applications.
Recommended next course or exam
Authorized Training PartnerCRS is a Red Hat Authorized Training Partner and winner of the Training Reseller Impact of the Year 2023 award for META.
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