#Artificial Intelligence (AI)

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.

v2026.1 was just released as GA, and one of the features I'm looking forward to using is the DTL Explainer feature.

This allows you to take a Data Transformation, and with a click of a button get a human-readable description of the transformation (which you can also use as the basis for the DTL Description).

For complex DTLs, especially ones you didn't write yourself, or you did but a long time ago, this will allow you to get a clear quick understanding of what it's doing.