Why Randomization is Key When Splitting Data for Machine Learning

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Essentially, Machine Learning is about learning from data. Having "good" data leads to better models, and more importantly, the quality of the information being used plays a crucial role in improving prediction accuracy.

Thank you community for translating an earlier article into Portuguese.
Am returning the favor with a new release of Pattern Match Workbench demo app.

Added support for Portuguese.

The labels, buttons, feedback messages and help-text for user interface are updated.

Pattern Descriptions can be requested for the new language.

Introducing Smart Clinical Sidechick — the intelligent, no-drama partner your EHR wishes it could be. She reads FHIR data in real time, interprets lab results without ghosting, and explains clinical alerts like she actually cares. Built with GPT-4 brains and YAML sass, she’s not here to replace your main EHR—just to make it look bad. Tired of irrelevant alerts and cryptic warnings? Sidechick serves up real, explainable insights, not vague “elevated risk” vibes. And when your backend crashes, she doesn’t panic—she self-heals.

This article shares analysis in solution cycle for the Open Exchange application TOOT ( Open Exchange application )

The hypothesis

A button on a web page can capture the users voice. IRIS integration could manipulate the recordings to extract semantic meaning that IRIS vector search can then offer for new types of AI solution opportunity.

Overview

With the help of SQL, you can build, train, and manage machine learning models directly in the database with InterSystems IRIS's robust IntegratedML tool. Using SQL examples that represent your data, we will go over IntegratedML configuration and how it is used in practical situations in this article.

Hi all,

Some days ago, I've seen a youtuber talking about how to create a neural network (sorry, is in spanish)

https://www.youtube.com/embed/iX_on3VxZzk
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Hi Community,

Watch this video to learn about the PainChek artificial intelligence technology, which assesses patient pain at the hospital bedside, leverages InterSystems IRIS interoperability to connect to third-party electronic medical record systems:

⏯ Using Artificial Intelligence / Machine Learning for Pain Assessment in a Clinical Environment @ Global Summit 2023

https://www.youtube.com/embed/-XNQUj7Sx_s
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Considering new business interest in applying Generative-AI to local commercially sensitive private data and information, without exposure to public clouds. Like a match needs the energy of striking to ignite, the Tech lead new "activation energy" challenge is to reveal how investing in GPU hardware could support novel competitive capabilities. The capability can reveal the use-cases that provide new value and savings.

Sharpening this axe begins with a functional protocol for running LLMs on a local laptop.

Hey Community,

We have more exciting news! The new InterSystems online programming contest dedicated to Generative AI, Vector Search and Machine Learning is starting very soon!

🏆 InterSystems Vector Search, GenAI and ML Contest 🏆

Duration: April 22 - May 19, 2024

Prize pool: $14,000

When trying to create a ML Model I ran into the problem, that AutoML apparantly isn't available on my IRIS instance.

I was able to use the following command successfully: CREATE MODEL ECLASSPREDICT PREDICTING (eClass) FROM SQLUSER.CRMSHOPARTIKEL

Then when trying to use TRAIN MODEL ECLASSPREDICT, I get the error from the image:

In the ever-evolving landscape of data science and machine learning, having the right tools at your disposal can make all the difference. In this article, we want to shine a spotlight on two essential Python libraries that have become indispensable for data scientists and machine learning practitioners alike: Matplotlib and scikit-learn.

For several years now Visual Studio Code has supported the notebook coding paradigm with a maturing UX and an API that is enabling a notebook extensions ecosystem to grow. One of the best-known notebook platforms is Jupyter Notebooks. A Microsoft team publishes an extension that allows VS Code to handle .ipynb notebook files. These can either work against a local Python environment or connect to a Jupyter Server, which typically hosts remote Python environments with beefier resources.

What if your InterSystems IRIS environments, whether local on your workstation or remote in your organization / cloud, could operate as Jupyter Servers? And not only for Embedded Python but also for ObjectScript and SQL

"If we build it, will they come?"

Hi community members!

I'm trying to deploy a container based on IRIS Community for Health ML image available from this url but when I start the container the memory consumption skyrockets to 99% making impossible to work with the instance (it never goes below the 95% of the memory). When I do the same with the IRIS Community for Health image it never goes over 80% of memory.