Operationalizing AI with OpenShift AI

Deploying an artificial intelligence model is only the first step; the real challenge lies in keeping that model reliable, scalable, and secure in a production environment. This text-based course guides you through the foundational concepts of operationalizing AI, helping you transition from experimental code to robust, automated deployment pipelines. You will discover how to manage infrastructure, automate workflows, and maintain model performance over time. By completing this course, you will understand how to bridge the gap between data science and IT operations, ensuring your AI initiatives deliver continuous business value. What you'll learn: - Understand the core principles of AI operationalization and lifecycle management - Configure cloud-native container environments to host and serve machine learning models - Implement automated pipelines to streamline model deployment and updates - Monitor model health, track performance drift, and manage system resources - Apply modern security best practices and access controls to AI workloads - Scale model serving infrastructure dynamically to handle varying production traffic You will begin by mastering essential terminology and core architectural concepts before moving on to practical deployment strategies and monitoring patterns. This structured approach ensures you build a solid theoretical foundation before exploring real-world operational workflows. This course is designed for IT professionals, system administrators, and developers who are new to operationalizing artificial intelligence. No prior experience with machine learning operations is required.