Generative AI Engineering with Azure Databricks

Generative AI is transforming how we build software, but scaling these models requires robust data engineering. Azure Databricks provides the perfect collaborative environment to process massive datasets and manage advanced language models efficiently. This text-based course guides you through the core workflows of modern AI engineering, preparing you to design and manage scalable AI systems. By reading through this comprehensive course, you will transition from understanding basic AI concepts to implementing end-to-end generative AI pipelines. You will learn how to leverage Apache Spark for data preparation, work with pre-trained models, and configure pipelines that scale to meet enterprise demands. What you'll learn: - Understand foundational generative AI concepts, LLM architectures, and cloud-based AI engineering workflows. - Configure Azure Databricks environments and utilize Apache Spark for scalable data preparation. - Implement Retrieval-Augmented Generation (RAG) patterns using modern vector database concepts. - Fine-tune pre-trained language models and track training runs using MLflow. - Evaluate model performance and safety metrics using systematic methodologies. - Deploy and integrate language models into production-ready applications. The course begins with key definitions, terminology, and setup procedures before moving into hands-on data preparation, model fine-tuning, and deployment strategies. You will progress through written explanations, analyze code snippets, and complete conceptual exercises designed to solidify your engineering skills. This course is designed for developers, data analysts, and aspiring AI engineers who are new to Azure Databricks. No prior experience with generative AI engineering or machine learning is required to begin. Start reading today to build your foundation in scalable generative AI engineering.