Beyond IntegratedML: What are our options for Continuous Training (CT) in IRIS?

Hello everyone,
I’m looking to implement Continuous Training (CT) as part of an MLOps strategy for some data science projects in IRIS. I want to automate the full cycle:

- Monitoring model performance & accuracy degradation.
- Retraining models automatically.
- Validating and updating production models.

I’ve looked into IntegratedML, but it seems more focused on the SQL interface for training (AutoML). Even with the new Custom Models (beta), which allows for more flexibility with Python, it doesn't seem to provide the "Continuous" orchestration out of the box.

I’d like to know:

1. Are there any established frameworks or "best practices" at the company for the CT components (triggers, automatic validation, model registry)?
2. Is this usually done manually using IRIS Interoperability (e.g., Business Processes/BPL) and Task Manager?
3. How are people handling Data Drift or Model Decay detection? Is there a standard way to do this with Embedded Python? (I'm exploring open source alternatives for this, such as MLFlow, and others)

I'm trying to avoid "reinventing the wheel" if there are already templates or patterns used here for custom Python MLOps.
Thanks!