Integrating AI with Red Hat OpenShift

*THIS IS A PRIVATE ONLY COURSE - CONTACT FOR PRICING* This intensive 4-Part course is designed for IT professionals, DevOps engineers, and AI practitioners who are keen to integrate AI applications with Kubernetes for scalable, robust, and efficient deployment. As the use of artificial intelligence (AI) continues to expand across industries, the need for managing AI workloads efficiently in production environments is critical. Kubernetes, as the leading container orchestration platform, offers an ideal framework to deploy, scale, and monitor AI applications.

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Through a combination of lectures, hands-on labs, and interactive sessions, participants will gain in-depth knowledge of Kubernetes fundamentals, explore best practices for deploying AI models on Kubernetes, and learn how to optimize and monitor machine learning (ML) and deep learning (DL) workloads in real-world settings. By the end of this course, participants will be equipped with the skills to successfully deploy and manage AI applications on Kubernetes and harness the full potential of this powerful integration. 

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Course Description: 

This 4-day, hands-on course is designed for IT professionals, DevOps engineers, and AI practitioners interested in deploying AI workloads on Red Hat OpenShift for scalable, resilient, and efficient production. OpenShift extends the power of Kubernetes by providing advanced features such as integrated CI/CD, enhanced security, and automated operations, making it an ideal platform for hosting machine learning (ML) and deep learning (DL) applications. 

Through in-depth lectures and hands-on labs, participants will explore OpenShift-specific features for deploying, scaling, and monitoring AI applications. Participants will learn how to utilize OpenShift’s built-in MLOps and DevOps capabilities to automate and manage AI workloads. By the end of the course, participants will have a solid foundation in deploying and managing AI applications on Red Hat OpenShift, enabling them to leverage this powerful platform in production environments. 

 

Learning Objectives: 

By the end of the course, participants will be able to: 

  1. Understand OpenShift Essentials: Grasp the fundamentals of Red Hat OpenShift, including its architecture, container orchestration, and OpenShift’s features that extend Kubernetes. 
  1. Deploy AI Models on OpenShift: Containerize and deploy AI models effectively on OpenShift to achieve scalable production workflows. 
  1. Leverage OpenShift MLOps Capabilities: Integrate CI/CD for automated AI model deployment and lifecycle management, using OpenShift Pipelines and GitOps. 
  1. Optimize and Scale AI Workloads: Utilize advanced OpenShift features such as GPU support, horizontal scaling, and auto-scaling for optimal AI model performance. 
  1. Monitor and Secure AI Workloads: Implement best practices and tools for monitoring and securing AI workloads in OpenShift to ensure performance, stability, and compliance. 

Part 1: Introduction to OpenShift and AI Workload Essentials 

Module 1: Red Hat OpenShift Fundamentals 

  • Overview of containers and OpenShift's role in container orchestration 
  • OpenShift architecture and components: Nodes, Pods, Routes, and Projects 
  • Understanding OpenShift’s developer and admin consoles 
  • Hands-on Lab: Set up an OpenShift cluster and deploy a basic application 

Module 2: Containerizing AI Workloads for OpenShift 

  • Docker containers for AI/ML models and dependencies (TensorFlow, PyTorch) 
  • Creating Dockerfiles and managing large model/data files 
  • Pushing AI model images to Red Hat Quay or OpenShift’s internal image registry 
  • Hands-on Lab: Containerize and push an AI model image to the OpenShift registry 

Discussion: 

  •  OpenShift's advantages for AI workloads. 
  • Assignment: Review an OpenShift YAML file for AI model deployment. 

 

Part 2: Deploying and Managing AI Models on OpenShift 

Module 1: AI Model Deployment and Service Exposure 

  • Deploying AI models as OpenShift Deployments, StatefulSets, and using persistent storage 
  • Configuring Routes to expose AI services and managing external traffic 
  • Utilizing OpenShift’s Service Mesh for advanced traffic management (if applicable) 
  • Hands-on Lab: Deploy a containerized AI model and expose it via an OpenShift Route 

Module 2: Resource and GPU Management 

  • Configuring resource quotas and limits for efficient resource usage 
  • Leveraging GPU support in OpenShift for optimized AI model performance 
  • Advanced scheduling options for AI workloads (node selectors, affinity rules) 
  • Hands-on Lab: Deploy a GPU-enabled AI model on an OpenShift cluster 

Discussion: 

  •  Best practices for resource management in OpenShift for AI workloads. 
  • Assignment: Update YAML configurations to utilize GPUs for AI models on OpenShift. 

 

Part 3: Scaling, Optimization, and MLOps in OpenShift 

Module 1: Scaling and Optimizing AI Workloads 

  • Horizontal and Vertical Pod Autoscaling in OpenShift 
  • Configuring OpenShift’s Machine Autoscaler for cluster auto-scaling 
  • Distributed training and workload optimization using OpenShift’s multi-node scheduling 
  • Hands-on Lab: Implement autoscaling for an AI model deployment on OpenShift 

Module 2: MLOps with OpenShift Pipelines and GitOps 

  • Introduction to MLOps and CI/CD with OpenShift Pipelines (Tekton) and GitOps (ArgoCD) 
  • Creating OpenShift Pipelines for automated model building and deployment 
  • Managing model versions and rollback strategies using GitOps 
  • Hands-on Lab: Set up a basic CI/CD pipeline for deploying AI models on OpenShift 

Discussion: 

  •  Integrating MLOps practices with OpenShift Pipelines. 
  • Assignment: Draft a GitOps pipeline configuration for a sample AI model. 

 

Part 4: Monitoring, Security, and Cluster Maintenance on OpenShift 

Module 1: Monitoring and Logging AI Workloads on OpenShift 

  • Setting up monitoring with OpenShift Monitoring Stack (Prometheus and Grafana) 
  • Implementing logging and alerting using EFK stack (Elasticsearch, Fluentd, Kibana) 
  • Analyzing metrics to monitor AI model performance and resource usage 
  • Hands-on Lab: Implement monitoring and logging for an AI model on OpenShift 

Module 2: Security and Cluster Maintenance 

  • Securing OpenShift deployments with Role-Based Access Control (RBAC) and network policies 
  • Managing Secrets and ConfigMaps for sensitive data in AI workloads 
  • Cluster maintenance best practices: patching, updates, and resource management 
  • Hands-on Lab: Implement security best practices and manage cluster updates for OpenShift 


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