Discussion Jorge Jaramillo Herrera · Feb 23

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:

#InterSystems IRIS #Documentation #InterSystems Ideas Portal #Learning Portal #Embedded Python #IntegratedML #Interoperability #Monitoring #Python #SQL
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Article Jorge Jaramillo Herrera · Jan 9 9m read

IRIS Dockerization and Embedded Python for Data Science — One-Command Setup for Reproducible ML Workflows

1-command only required for an entire IRIS instance for Data Science projects, and leveraging this to compare query methods' speed (Dynamic SQL, Pandas Query, and Globals).

Before joining InterSystems, I worked in a team of web developers as a data scientist. Most of my day-to-day work involved training and embedding ML models in Python-based backend applications through microservices, mainly built with the Django framework and using Postgres SQL for sourcing the data.

#InterSystems IRIS #Docker #Embedded Python #Globals #Multi-model #ObjectScript #Performance #Python #SQL
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