OpenShift AI Administration

The OpenShift AI Administration course is a comprehensive online training program designed for system administrators, platform engineers, DevOps professionals, MLOps engineers, cloud administrators, and IT operations teams responsible for deploying, managing, and supporting enterprise AI and machine learning platforms. The course provides an in-depth understanding of Red Hat OpenShift AI, enabling participants to build, administer, secure, monitor, and optimize AI infrastructure running on Red Hat OpenShift Container Platform.

Throughout the training, participants will explore the architecture, components, and capabilities of OpenShift AI, including Data Science Projects, Workbenches, Model Serving, Pipelines, Cluster Storage, GPU integration, and AI application lifecycle management. Learners will gain practical knowledge of how OpenShift AI enables organizations to deliver scalable, secure, and production-ready environments for data scientists, machine learning engineers, and AI developers.

The course begins with the fundamentals of OpenShift AI, introducing its architecture, deployment models, supported infrastructure, and integration with Kubernetes and Red Hat OpenShift. Participants will learn how OpenShift AI supports collaborative AI development while simplifying the deployment and management of machine learning workloads across enterprise environments.

Students will learn how to install and configure OpenShift AI using the Operator Framework, validate platform readiness, configure storage classes, integrate object storage, and prepare compute resources for AI workloads. The course explains infrastructure planning, resource allocation, and cluster preparation to ensure high availability and optimal platform performance.

A major focus of the course is user and project administration. Participants will learn how to create and manage data science projects, provision development workspaces, configure authentication, implement Role-Based Access Control (RBAC), manage user permissions, and establish secure multi-tenant environments. Best practices for organizing projects, controlling access, and supporting collaboration between administrators, developers, and data scientists are also covered.

The training explores the administration of Data Science Workbenches and Jupyter Notebook environments. Learners will configure notebook images, customize development environments, manage resource quotas, attach persistent storage, and troubleshoot notebook lifecycle issues. The course also demonstrates how administrators can optimize notebook performance while ensuring efficient resource utilization across multiple teams.

Participants will gain extensive experience managing AI infrastructure resources, including CPU, memory, GPU, storage, and networking. The course explains Kubernetes scheduling, node management, resource limits, autoscaling strategies, and workload optimization techniques that improve performance and maximize infrastructure efficiency for demanding AI and machine learning workloads.

Model deployment and serving are covered in detail. Students will learn how to publish trained machine learning models, configure inference services, manage model versions, perform rolling updates, expose secure APIs, and monitor model performance in production environments. The course also explains deployment strategies that support scalable, highly available AI services for enterprise applications.

The program includes comprehensive coverage of data management and storage integration. Participants will configure object storage for datasets and models, connect external databases and data sources, manage persistent volumes, implement secure credential management using Kubernetes Secrets, and establish reliable data pipelines that support enterprise AI workflows.

Security is emphasized throughout the course. Learners will implement authentication and authorization mechanisms, configure TLS certificates, enforce network policies, secure API endpoints, manage sensitive credentials, and apply enterprise security best practices to protect AI workloads. The course also introduces governance, auditing, compliance requirements, and operational controls necessary for regulated enterprise environments.

Monitoring, observability, and troubleshooting are essential skills developed during the training. Participants will configure logging, metrics collection, dashboards, alerts, and health monitoring for OpenShift AI clusters and workloads. They will learn systematic troubleshooting techniques for resolving installation, networking, storage, notebook, model serving, and infrastructure issues while maintaining platform stability and service availability.

The course also covers platform maintenance, backup, disaster recovery, upgrades, patch management, and lifecycle administration. Students will learn how to perform platform updates with minimal downtime, protect critical AI assets, recover from failures, and implement maintenance strategies that ensure long-term reliability and operational excellence.

By the end of this course, participants will possess the practical knowledge and administrative skills required to deploy, configure, secure, monitor, troubleshoot, and optimize OpenShift AI environments. They will be capable of supporting enterprise-scale AI and machine learning initiatives while ensuring high availability, security, scalability, and operational efficiency. This training prepares professionals to confidently administer production-ready OpenShift AI platforms that meet the growing demands of modern artificial intelligence, data science, and cloud-native application environments.

What You Will Learn

• Introduction to OpenShift AI
• Installing and Configuring OpenShift AI
• User and Project Management
• Data Science Workbench Administration
• Managing AI Workloads
• Model Serving and Deployment
• Data Connections and Storage
• Monitoring and Performance Optimization