InterSystems IntegratedML is an all-SQL machine learning (ML) module for InterSystems IRIS or IRIS for Health that: - Gives users the ability to create, train and deploy powerful models from simple SQL syntax without requiring data scientists. - Wraps "best of breed" open source and proprietary "AutoML" frameworks including DataRobot. - Focuses on easy deployment to IRIS, so you can easily add machine learning to your applications.
This is the second post of a series explaining how to create an end-to-end Machine Learning system.
Exploring Data
The InterSystems IRIS already has what we need to explore the data: an SQL Engine! For people who used to explore data in
csv or text files this could help to accelerate this step. Basically we explore all the data to understand the intersection
(joins) which should help to create a dataset prepared to be used by a machine learning algorithm.
Following up the previous part, it's time to take advantages for IntegratedML VALIDATION MODEL statement, to provide information in order to monitor your ML models. You can watch it in action here
Currently, the process of using machine learning is difficult and requires excessive consumption of data scientist services. AutoML technology was created to assist organizations in reducing this complexity and the dependence on specialized ML personnel.
AutoML allows the user to point to a data set, select the subject of interest (feature) and set the variables that affect the subject (labels). From there, the user informs the model name and then creates his predictive or data classification model based on machine learning.
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