Airflow 3 brings several exciting new features that better support MLOps: Native, intuitive backfills Removal of the unique execution date for dag runs Native support for event-driven scheduling These features, combined with the Airflow AI SDK, enable dag authors to easily build scalable, maintainable, and performant LLMOps pipelines. In this talk, we’ll go through a series of workflows that use the Airflow AI SDK to empower Astronomer’s support staff to more quickly resolve problems faced by Astronomer’s customers.

Ready to contribute to Apache Airflow? In this hands-on workshop, you’ll be expected to come prepared with your development environment already configured (Breeze installed is strongly recommended, but Codespaces works if you can’t install Docker). We’ll dive straight into finding issues that match your skills and walk you through the entire contribution process—from creating your first pull request to receiving community feedback. Whether you’re writing code, enhancing documentation, or offering feedback, there’s a place for you. Let’s get started and see your name among Airflow contributors!