As organizations deploy machine learning and AI systems at scale, managing complex data pipelines, model training workflows, and automated decision systems becomes increasingly challenging. Apache Airflow has emerged as a powerful orchestration platform for coordinating data engineering, analytics, and AI workflows across distributed cloud environments.

This session explores how Airflow can orchestrate enterprise AI pipelines, from data ingestion and feature engineering to model training, evaluation, and deployment. Drawing from real-world enterprise transformation programs, the talk will highlight architectural patterns for integrating Airflow with modern AI platforms, cloud-native infrastructure, and governance frameworks.

Attendees will gain practical insights into designing reliable AI workflows, managing dependencies across complex data pipelines, and scaling orchestration for production-grade machine learning systems.