GenAIOps: Operationalizing Generative AI Applications

Moving a generative AI model from a simple prompt to a reliable production application requires more than just clean code. As organizations adopt Large Language Models, mastering the lifecycle of deployment, monitoring, and evaluation—known as GenAIOps—is essential for building robust AI systems. This text-based course guides you through the foundational principles of GenAIOps, enabling you to transition prototype AI applications into stable, secure, and observable production services. What you'll learn: - Understand core GenAIOps concepts, terminology, and lifecycle phases. - Manage and version prompts systematically to ensure consistent model behavior. - Evaluate model outputs using automated metrics and validation frameworks. - Implement tracing and observability to debug complex AI workflows in real time. - Set up continuous integration and automated testing for generative AI pipelines. - Configure basic guardrails and security practices to protect production systems. You will start with foundational AI definitions and lifecycle planning before exploring practical, step-by-step written explanations of prompt management, evaluation strategies, and production monitoring. This course is designed for software developers, system administrators, and technical beginners eager to understand how to operationalize AI, with no prior machine learning experience required. Start reading today to bridge the gap between AI prototyping and production-grade reliability.