Hello, I work on an open-source project called Flyte <https://github.com/flyteorg/flyte>, which is a container-native, structured programming and distributed processing platform that enables highly concurrent, scalable, and maintainable workflows for machine learning and data processing pipelines.
As a more significant chunk of the users who are into *pipelines* are using Airflow, we've been thinking about building a provider that bridges the gap between Airflow and Flyte, to help the Airflow users retain their existing pipelines for ETL and use Flyte from within the Airflow DAGs to run machine learning jobs (say). At Lyft, where Airflow and Flyte are used together, they extensively use this operator to enable Airflow DAGs interop with Flyte. Lately, our users have also been requesting for this feature. We've had this operator in the back of our minds for a long time; here's the issue <https://github.com/flyteorg/flyte/issues/544>. The Flyte team would like to know the community's thoughts on this provider. Many thanks, Samhita
