LLMOps Foundations: Deploy and Manage Large Language Models

Large Language Models are transforming how we build software, but moving them from a prototype to a reliable production system requires specialized operational skills. This text-based course guides you through the core principles of LLMOps, helping you bridge the gap between AI development and robust system operations. By reading through clear explanations and analyzing practical code patterns, you will understand how to manage model lifecycles, optimize prompts, and integrate external data securely. You will transition from manual prompting to building structured, maintainable, and observable LLM pipelines. What you'll learn: - Understand foundational LLM concepts and the core lifecycle of LLMOps - Configure retrieval-augmented generation (RAG) patterns with vector databases - Apply structured prompt engineering techniques within automated workflows - Evaluate model performance, latency, and cost using modern testing strategies - Monitor production LLM applications for drift, bias, and unexpected behavior - Implement basic CI/CD pipelines tailored for AI-driven software. The journey begins with essential AI and operational definitions before moving step-by-step through deployment architectures, evaluation frameworks, and continuous monitoring practices. This course is designed specifically for beginners, software engineers, and system administrators looking to enter the AI operations space with no prior LLMOps experience required. Start reading today to master the infrastructure behind modern language models.