LLMOps Foundations in Azure Databricks

As Large Language Models (LLMs) transition from prototypes to production, managing their lifecycle becomes a critical engineering challenge. This written course guides you through operationalizing language models efficiently using cloud-scale data platforms. You will transition from understanding basic LLM concepts to structuring complete, automated pipelines. By reading through practical examples, you will learn how to manage prompts, track model versions, and deploy LLM applications with confidence. What you'll learn: 1. Understand foundational LLMOps concepts, terminology, and lifecycle stages. 2. Configure MLflow to track prompts, parameters, and model outputs. 3. Implement Retrieval-Augmented Generation (RAG) architectures using vector databases. 4. Deploy and serve LLMs as scalable REST endpoints within Azure Databricks. 5. Monitor model performance and safety metrics in production environments. The curriculum starts with essential definitions and core LLMOps principles before guiding you through hands-on configuration, orchestration, and monitoring patterns. You will study structured code examples and step-by-step deployment workflows designed for real-world application. This course is designed for data engineers, software developers, and aspiring machine learning engineers who are new to LLM operations. No prior experience with Azure Databricks is required, though a basic familiarity with Python is helpful. Start reading today to master the essentials of modern LLMOps.