LLMOps Fundamentals: Deploying and Managing Large Language Models

Large Language Models are transforming software, but moving them from a simple prompt 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 prototyping and production-grade deployments. By the end of this course, you will transition from writing simple prompts to understanding how to operationalize entire LLM workflows, structuring data pipelines, integrating vector databases, and implementing robust monitoring strategies. What you'll learn: - Understand the core differences between traditional DevOps, MLOps, and LLMOps lifecycle patterns. - Implement Retrieval-Augmented Generation (RAG) architectures to connect models with external data sources. - Configure vector databases to store, index, and retrieve semantic information efficiently. - Evaluate LLM outputs using modern benchmarking techniques and automated feedback loops. - Monitor production LLM applications for cost, latency, drift, and unexpected behaviors. - Apply prompt engineering best practices and version control to manage model prompts systematically. This course starts with essential terminology and the foundational concepts of LLM architectures, then progresses step-by-step through data ingestion, orchestration, deployment, and ongoing system maintenance. It is designed for developers, data professionals, and tech enthusiasts new to machine learning operations, with no prior experience in LLMOps required. Start reading today to build reliable, scalable, and secure language model applications.