Orchestrating GenAI Workflows with Apache Airflow

Moving a generative AI prototype from a manual script to a reliable, automated pipeline is a major hurdle in modern AI development. This course teaches you how to bridge the gap between AI modeling and robust engineering. You will learn how to automate, monitor, and scale your AI workflows, transforming experimental scripts into production-ready pipelines. Starting with the core concepts of workflow orchestration and Apache Airflow fundamentals, you will build a solid foundation in task scheduling, dependency management, and pipeline design. You will then explore how to integrate modern AI components, including API calls to large language models, retrieval-augmented generation (RAG) processes, and vector database updates. What you will learn: Understand the foundational principles of workflow orchestration and Directed Acyclic Graphs (DAGs); Configure Apache Airflow environments and write clean, maintainable pipeline code; Integrate generative AI APIs and manage API rate limits and retries within your workflows; Automate data preparation and vector database ingestion pipelines for RAG systems; Implement error handling, logging, and monitoring to ensure high pipeline reliability; Apply modern best practices for testing and deploying workflows securely. This text-based guide begins with essential terminology and structural concepts before guiding you through structured, practical workflow scenarios. This course is designed for software developers, data engineers, and AI enthusiasts who want to automate their AI applications; no prior experience with Apache Airflow is required. Start building reliable, automated pipelines for your generative AI applications today.