For more than 20 years Michael has built, developed and architected data platforms for machine learning teams across many industries and around the world. As the challenges of ML implementation shift from model development to ML operations and in light of the proliferation of foundational models like LLMs, one thing is more clear than ever: the ability to weave ML into the fabric of an organization depends on the ability to orchestrate data flows across platform, cloud, organizational and functional boundaries. Today Michael uses a strong background in data science and machine learning to grow the field of ML Orchestration (MLOX) on the strong foundation built by the Airflow community and Astronomer.