Hi everyone, I'm trying to use Prefect for our ML pipelines. We train our ~20 (and growing) models on a weekly basis and I'm experimenting with some setups that could make sense. Our pipeline runs different models by providing a configuration file. Conceptually, I would say that it would make sense to have a single deployment that defines a flow that takes the configuration as an input and is then scheduled to be run for each configuration. However, providing parameters in the schedule is not supported. Alternatively, I wanted to create a flow that launches other flows and adds them to the work queue but I couldn't find how to do this in the documentation. Does anyone know how to do this or have an alternative approach. I suppose that we are not the first team that has a ML pipeline that runs for different models. Best, Mark

However, providing parameters in the schedule is not supported.

This is seems to a be a pretty popular request from our users. Would you mind adding a +1 to this feature request in GitHub?

Thanks for the response. The worker pattern doesn't seem ideal because it seems that flows are more observable than tasks. Furthermore, this approach complicates launching ad hoc jobs. The per-schedule parameter feature seems ideal for this use-case because it makes use of flows, only requires a single deployment and supports ad hoc pipeline runs via the "quick run" button of a flow. For now, I will just create a deployment for each model even though it's not the most ergonomic solution.