A Continuous Training (CT) pipeline formalises a Machine Learning (ML) model developed through data science experimentation, using the data available at a given point in time. It prepares the model for deployment while enabling autonomous updates as new data becomes available, along with robust performance monitoring, logging, and model registry capabilities for auditing purposes.

InterSystems IRIS already provides nearly all the components required to support such a pipeline. However, one key element is missing: a standardised tool for model registry.

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: