We’ve all watched our Airflow DAGs grow from simple pipelines into complex beasts that nobody wants to touch. But what if AI could be your DAG whisperer? In this session, I’ll show you how we’re teaching machines to speak Airflow.
By combining the pattern-recognition superpowers of Large Language Models with traditional code analysis, we’ve built a framework that doesn’t just find problems—it fixes them. Think of it as a smart co-pilot for your data orchestration that catches missing dependencies before they cause midnight alerts and suggests restructuring that actually makes sense.
This isn’t theoretical—we’ve seen 40% fewer pipeline failures and 30% faster execution times when teams adopt these approaches. I’ll walk through real examples where AI spotted inefficient task groupings and resource bottlenecks that humans had missed for months. You’ll discover practical ways to build AI validation into your workflow, train models to recognize Airflow’s quirks, and even generate documentation that people actually want to read. Best of all, you’ll leave with code samples you can implement right away and a framework for building your own AI assistants.
Supriya Badgujar
Manager at Blue Altair
Vishesh Garg
Manager-Data Architect at EXL Analytics