Airflow as the orchestration tool for Data and AI

​Session Description:

​As teams push more data pipelines and machine learning into production, Apache Airflow has become the standard for coordinating it all. This session shows how Airflow's DAG model, dynamic task mapping, and data-aware scheduling unify traditional ETL with modern AI workflows—from model retraining to LLM and RAG pipelines. You'll leave with practical patterns for building orchestration that's resilient, observable, and ready to scale.

​Speaker Bio: Shrividya Hegde (Shri) | LinkedIn

​Shrividya is a Senior AI Data Engineer specializing in building and scaling production AI systems. She designs robust data pipelines with Airflow and Python, and brings deep expertise in evaluating RAG and LLM-powered applications, ensuring the systems behind AI products are reliable, tested, and production-ready. Drawing on a strong foundation in software quality and automation, Shri bridges the gap between engineering rigor and AI innovation. Shri is an active member of the Women in Data community, recognized as an Apache Airflow Champion at a global level, writer and speaker in various conferences championing more diverse voices in data and AI.