MLOps and LLMOps with Databricks and LangChain

Managing machine learning and generative AI applications in production requires robust engineering workflows and scalable platforms. This text-based course guides you through foundational concepts and practical techniques for managing models, tracking experiments, and operationalizing generative solutions on Databricks. You will build a clear understanding of machine learning operations (MLOps) and large language model operations (LLMOps), discovering how to move projects smoothly from initial prototyping to structured deployment. What you'll learn: - Understand core MLOps and LLMOps terminology, lifecycle stages, and platform architecture - Track experiments, parameters, metrics, and model versions with MLflow - Construct retrieval-augmented generation (RAG) pipelines using LangChain and modern language models - Configure Databricks vector search and data pipelines for generative workflows - Deploy, manage, and monitor model endpoints across staging and production environments - Apply governance, prompt management, and continuous evaluation practices to AI systems. The course begins with essential definitions and platform basics before progressing through experiment management, pipeline orchestration, and modern LLM development. It is written for developers, data analysts, and aspiring machine learning engineers looking for a practical entry point into enterprise AI operations without complex prerequisites. Start reading today to structure and scale your machine learning workflows.