Serving Models at Scale with Red Hat AI Enterprise (AI510)
- Course Code AI510
- Duration 2 days
Course Delivery
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Course Delivery
This course is available in the following formats:
-
Company Event
Event at company
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Public Classroom
Traditional Classroom Learning
Request this course in a different delivery format.
Course Overview
TopDeploying AI models in enterprise environments demands more than a working prototype.
Organizations struggle to move AI models from experimentation to production at scale, resulting in delayed time-to-value and underutilized infrastructure investments.
This course equips teams to deploy, optimize, and operate AI models efficiently on Red Hat AI, reducing the gap between prototype and production and enabling reliable, cost-effective model serving across the enterprise.
Organizations must evaluate serving runtimes, optimize models for production hardware, manage inference at scale, and monitor deployed models to maintain performance and reliability.
This 2 days hands-on course teaches students to deploy, serve, and manage AI models at enterprise scale by using Red Hat OpenShift AI.
Students gain hands-on experience with the AI Factory framework, model serving with vLLM and KServe, NVIDIA NIM microservices, distributed inference with llm-d, workload scheduling with Kueue, and production model monitoring.
This course is based on Red Hat OpenShift AI 3.4.
Updated 01/10/2026
Course Schedule
TopTarget Audience
TopThis course is designated for AI Platform Engineers, ML operations teams, and infrastructure administrators responsible for deploying, serving, and managing AI models at scale on Red Hat OpenShift AI.
Course Objectives
TopAfter completing this course, participants should be able to:
- Evaluate serving runtimes,
- Optimize and deploy generative and predictive AI models at scale using vLLM, KServe, and NVIDIA NIM on Red Hat OpenShift AI,
- Configure distributed inference and workload scheduling,
- Monitor deployed models in production.
Course Content
TopImplementation: Implementing enterprise design patterns and structures for scalable AI operations.
Model Serving Runtimes: Evaluating and utilizing serving runtimes using vLLM and KServe.
NVIDIA NIM Microservices: Integrating optimized NVIDIA inference microservices for high-performance generative AI workloads.
Distributed Inference: Managing scalable, multi-node inference execution using llm-d.
Workload Scheduling: Optimizing GPU and compute resource scheduling with Kueue.
Production Monitoring: Tracking performance, reliability, and metrics for deployed models in production.
Course Prerequisites
Top- Familiarity with Red Hat OpenShift concepts, or completion of the Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288) course.
- Basic understanding of large language models, AI/ML concepts, and model serving.
- The Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) course is recommended for students who are new to the Red Hat OpenShift AI platform.
Confirmation of the correct skill set knowledge can be obtained by passing the online skills assessment at Red Hat Skills Assessment
Test Certification
TopNo certification associated with this course
Follow on Courses
TopNo specific recommendation
Further Information
TopOfficial course book provided to participants
