#Machine Learning (ML)

As we all know, InterSystems is a great company.

Their products can be just as useful as they are complex.

Yet, our pride sometimes prevents us from admitting that we might not understand some concepts or products that InterSystems offers for us.

Today we are beginning a series of articles explaining how some of the intricate InterSystems products work, obviously simply and clearly.

In this essay, I will clarify what Machine Learning is and how to take advantage of it.... because this time, you WILL KNOW for sure what I am talking about.

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.

Apache Spark has rapidly become one of the most exciting technologies for big data analytics and machine learning. Spark is a general data processing engine created for use in clustered computing environments. Its heart is the Resilient Distributed Dataset (RDD) which represents a distributed, fault tolerant, collection of data that can be operated on in parallel across the nodes of a cluster. Spark is implemented using a combination of Java and Scala and so comes as a library that can run on any JVM.

Last week saw the launch of the InterSystems IRIS Data Platform in sunny California.

For the engaging eXPerience Labs (XP-Labs) training sessions, my first customer and favourite department (Learning Services), was working hard assisting and supporting us all behind the scene.

Hi all. Yesterday I tried to connect Apache Spark, Apache Zeppelin, and InterSystems IRIS. During the process, I experienced troubles connecting it all together and I did not find a useful guide. So, I decided to write my own.

Preview releases are now available for InterSystems IRIS Advanced Analytics, and InterSystems IRIS for Health Advanced Analytics! The Advanced Analytics add-on for InterSystems IRIS introduces IntegratedML as a key new feature.

The last time that I created a playground for experimenting with machine learning using Apache Spark and an InterSystems data platform, see Machine Learning with Spark and Caché, I installed and configured everything directly on my laptop: Caché, Python, Apache Spark, Java, some Hadoop libraries, to name a few. It required some effort, but eventually it worked.

Hi everyone,

I am very pleased to announce that the Readmission Demo has been released as open source. Many thanks to the Solution Factory team that worked hard on making this possible.

Here are the changes:

Hey Developers!

We're pleased to invite you all to the next competition of creating open-source solutions using InterSystems IRIS! Please join:

🏆 InterSystems Analytics Contest 🏆

Duration: December 7 - 27, 2020

Introduction

In InterSystems IRIS 2024.3 and subsequent IRIS versions, the AutoML component is now delivered as a separate Python package that is installed after installation. Unfortunately, some recent versions of Python packages that AutoML relies on have introduced incompatibilities, and can cause failures when training models (TRAIN MODEL statement). If you see an error mentioning "TypeError" and the keyword argument "fit_params" or "sklearn_tags", read on for a quick fix.

This series of articles would cover Python Gateway for InterSystems Data Platforms. Leverage modern AI/ML tools and execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:

Fixing the terminology

A robot is not expected to be either huge or humanoid, or even material (in disagreement with Wikipedia, although the latter softens the initial definition in one paragraph and admits virtual form of a robot). A robot is an automate, from an algorithmic viewpoint, an automate for autonomous (algorithmic) execution of concrete tasks. A light detector that triggers street lights at night is a robot. An email software separating e-mails into “external” and “internal” is also a robot. Artificial intelligence (in an applied and narrow sense, Wikipedia interpreting it differently again) is algorithms for extracting dependencies from data. It will not execute any tasks on its own, for that one would need to implement it as concrete analytic processes (input data, plus models, plus output data, plus process control). The analytic process acting as an “artificial intelligence carrier” can be launched by a human or by a robot. It can be stopped by either of the two as well. And managed by any of them too.

Hi Community,
In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

• Step1: Install required libraries

• Step2: Create patterns and responses file

• Step3: Train the Model

• Step4: Create ChatBot Application based on the trained model

So Let us start.

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