Foundations of LLMOps: Deploying and Managing AI Models

Scaling large language models from a local prototype to a reliable production service requires a specific set of operational skills. This course provides the foundational knowledge needed to manage the lifecycle of AI-driven applications effectively. You will transition from simply using AI models to understanding the infrastructure, monitoring, and deployment strategies that keep them running. By reading through these lessons, you will gain the confidence to handle model versioning, performance optimization, and the integration of external data sources. What you'll learn: - Understand the core principles of LLMOps and how they differ from traditional software operations - Deploy large language models using modern containerization and orchestration techniques - Implement Retrieval-Augmented Generation (RAG) patterns to enhance model accuracy - Manage vector databases for efficient information retrieval and storage - Monitor model performance and latency to ensure high-quality user experiences - Apply prompt engineering best practices to optimize model outputs systematically - Configure automated pipelines for model testing and continuous integration The material begins with essential terminology and the basic architecture of language models before moving into practical deployment workflows and observability. You will progress through written explanations of infrastructure setup, data handling, and model maintenance strategies. This course is designed for beginners interested in the operational side of AI; no prior experience with model deployment is required. Start building your expertise in the critical field of AI operations today.