It has been nearly 4 years since the launch of Managed Workflows for Apache Airflow (MWAA) by AWS. It has gone through the trials and tribulations as with any new idea, working with customers to better understand its shortcomings, building dedicated teams focused on scaling and growth, and at its core, preserving the integrity and functionality of Apache Airflow. Initially launched with Airflow 1.10, MWAA is now available globally in multiple AWS regions supporting the latest version of Airflow along with a multitude of features. In this talk, we will cover a bit of that history along with debunking a few myths surrounding the critical needs for users today. From compliance requirements, larger environments, observability, and pricing, we will discuss how MWAA has evolved and continues to grow through its focus on customer value and more importantly, its dedication to the Apache Airflow community.

AI workloads are becoming increasingly complex, with unique requirements around data management, compute scalability, and model lifecycle management. In this session, we will explore the real-world challenges users face when operating AI at scale. Through real-world examples, we will uncover common pitfalls in areas like data versioning, reproducibility, model deployment, and monitoring. Our practical guide will highlight strategies for building robust and scalable AI platforms leveraging Airflow as the orchestration layer and AWS for its extensive AI/ML capabilities. We will showcase how users have tackled these challenges, streamlined their AI workflows, and unlocked new levels of productivity and innovation.