GenAI Ops: Deploying and Managing Generative AI Systems

Transitioning generative AI from a prototype to a reliable production system requires more than just writing prompts. Organizations need robust operations to ensure AI applications are secure, scalable, and cost-effective. This text-based course guides you through the foundational principles of GenAI Ops (Generative AI Operations). You will learn how to manage large language models, integrate vector databases, set up retrieval-augmented generation (RAG) pipelines, and implement essential security and monitoring workflows. What you'll learn: - Understand the core differences between traditional MLOps and GenAI Ops - Configure and optimize retrieval-augmented generation architectures - Implement security guardrails to protect model inputs and outputs - Monitor AI system performance, latency, and operational costs - Manage prompt versions and model evaluations systematically - Deploy generative AI applications using modern container and orchestration concepts You will start with key terminology and foundational concepts before moving into practical architectures, evaluation frameworks, and deployment workflows. Each module reinforces your learning through clear, step-by-step written explanations and conceptual walkthroughs. This course is designed for software developers, system administrators, and technology enthusiasts who are new to AI operations. No advanced background in machine learning is required. Start reading today to build the skills needed to manage modern generative AI systems.