Generative AI Engineering with Azure Databricks

Organizations everywhere are looking to leverage the power of large language models to build intelligent applications. This text-based course guides you through the core concepts of generative AI engineering using the unified Azure Databricks platform. You will transition from understanding basic AI concepts to designing and implementing retrieval-augmented generation (RAG) systems and deploying LLMs. You will gain the practical knowledge required to manage AI data pipelines, configure vector search, and orchestrate secure AI workflows. What you will learn: Understand the foundational concepts of generative AI, transformer architectures, and large language models. Configure Azure Databricks environments for scaling machine learning and AI workloads. Implement Retrieval-Augmented Generation (RAG) patterns to connect LLMs with custom enterprise data. Build and query vector databases using Databricks Vector Search for efficient information retrieval. Manage and track model development using MLflow for seamless deployment and monitoring. Apply prompt engineering techniques and evaluate model outputs for accuracy and safety. The course starts with essential generative AI terminology and architecture basics before moving into practical data preparation, vector indexing, and pipeline orchestration. You will read through clear explanations, conceptual breakdowns, and realistic code configurations. This course is designed for data engineers, software developers, and technology enthusiasts who want to start building generative AI solutions. No prior experience with Databricks or machine learning is required, though a basic familiarity with Python is helpful. Start your journey into generative AI engineering today.