OpenShift AI Deployment and Model Management for Beginners

Organizations are rapidly adopting containerized platforms to scale their artificial intelligence and machine learning operations. Understanding how to manage these environments is a critical skill for modern systems administrators and platform engineers. This course provides a clear, text-based guide to setting up and administering OpenShift AI, preparing you to support data science teams effectively. You will transition from understanding basic cloud-native AI concepts to confidently managing the entire lifecycle of machine learning models in a cluster environment. What you'll learn: - Understand the core architecture and foundational components of OpenShift AI - Configure and deploy the OpenShift AI operator within a cluster - Manage user access, resource quotas, and secure storage for data science projects - Deploy and serve pre-trained machine learning models as scalable APIs - Integrate modern model-serving runtimes and monitor resource consumption - Implement basic GitOps patterns for reproducible AI infrastructure configuration This course begins with essential definitions of cloud-native AI infrastructure before guiding you through step-by-step configuration workflows, user management, and model deployment scenarios. You will learn through clear, written explanations, configuration snippets, and practical administration exercises. This course is designed for system administrators, DevOps engineers, and platform professionals who are new to OpenShift AI and want to build foundational skills. No prior machine learning experience is required, though basic familiarity with container orchestration is helpful. Start reading today to master cloud-native AI platform administration.