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.
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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.
The Customer Churn Predictor application is a tool that demonstrates how to use IntegratedML to helps companies identify which customers are likely to leave in the near future and develop customer retention strategies. Benefits of the similar application include:
In Episode 11 of Data Points, UX designer @Ksenia Samokhvalova joins the podcast to talk about the approach to user experience at InterSystems, how it may differ from commonly considered UX concepts, and what her team is doing to constantly improve usability with the developer's goals in mind. If you'd like to take a quick survey to get involved with UX testing for InterSystems technologies, you can do that here!
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This document mainly enriches the content of the previous article and introduces the use of the application.
Perhaps you have already read the previous article, but I still want to say, After completing the initialization operation (including model creation and training), the Fhir HepatitisC Predict application then predicts HepatitisC
Watch this video to learn how to build and run machine-learning models using InterSystems IRIS Cloud IntegratedML using the example of 15,000 rows of data from New York City taxi rides to predict the tip for a given ride:
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I'd like to know if the VALIDATE MODEL statement in IntegratedML performs a cross validation (CV) procedure internally. If it doesn't, is there a ready to use method for CV?
New to machine learning? Watch this video on InterSystems Developers YouTube to understand the basic concepts of machine learning and how it provides value in applications around the world today:
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Continued from the previous Part I ... In part I, we walked through traditional ML approaches on this Covid-19 dataset on Kaggle.
In this Part II, let's run the same data & task, in its simplest possible form, through IRIS integratedML which is a nice & sleek SQL interface for backend AutoML options. It uses the same environment.
I've been playing with IntegratedML and have created a model and trained the model. When I try to use PREDICT or PROBABILITY statements in an SQL query, I get the following error:
[SQLCODE: <-400>:<Fatal error occurred>]
[%msg: <PREDICT execution error: ERROR #5002: ObjectScript error: <OBJECT DISPATCH>%LoadModel+31^%ML.AutoML.TrainedModel.1 *<class 'AttributeError'>: 'str' object has no attribute 'decode' - >]
Here's an example of the sequence of steps I've followed that lead to the error:
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