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#Machine Learning (ML)

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Machine learning (ML) is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" with data, without being explicitly programmed.

Learn more.

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.

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Article
· Jan 26, 2024 8m read
PrivateGPT exploring the Documentation

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.

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Last week, we announced the InterSystems IRIS Data Platform, our new and comprehensive platform for all your data endeavours, whether transactional, analytics or both. We've included many of the features our customers know and loved from Caché and Ensemble, but in this article we'll shed a little more light on one of the new capabilities of the platform: SQL Sharding, a powerful new feature in our scalability story.

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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.

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In today's data landscape, businesses encounter a number of different challenges. One of them is to do analytics on top of unified and harmonized data layer available to all the consumers. A layer that can deliver the same answers to the same questions irrelative to the dialect or tool being used.

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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.

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In this article you will have access to the curated base of articles from the InterSystems Developer Community of the most relevant topics to learning InterSystems IRIS. Find top published articles ranked by Machine Learning, Embedded Python, JSON, API and REST Applications, Manage and Configure InterSystems Environments, Docker and Cloud, VSCode, SQL, Analytics/BI, Globals, Security, DevOps, Interoperability, Native API. Learn and Enjoy!

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Many factors affect a person's quality of life, and one of the most important is sleep. The quality of our sleep determines our ability to function during the day and affects our mental and physical health. Good quality sleep is critical to our overall health and well-being. Therefore, by analyzing indicators preceding sleep, we can determine the quality of our sleep. This is precisely the functionality of the Sheep's Galaxy application.

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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.

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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.

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Hi Community

In this article, I will introduce my application IRIS-GenLab.

IRIS-GenLab is a generative AI Application that leverages the functionality of Flask web framework, SQLALchemy ORM, and InterSystems IRIS to demonstrate Machine Learning, LLM, NLP, Generative AI API, Google AI LLM, Flan-T5-XXL model, Flask Login and OpenAI ChatGPT use cases.

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image

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.

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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.

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Article
· Jul 27, 2018 4m read
Load a ML model into InterSystems IRIS

Hi all. Today we are going to upload a ML model into IRIS Manager and test it.

Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, Python 3.6.5.

Introduction

These days many available different tools for Data Mining enable you to develop predictive models and analyze the data you have with unprecedented ease. InterSystems IRIS Data Platform provide a stable foundation for your big data and fast data applications, providing interoperability with modern DataMining tools.

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On this GitHub you can find all the information on how to use a HuggingFace machine learning / AI model on the IRIS Framework using python.

1. iris-huggingface

Usage of Machine Learning models in IRIS using Python; For text-to-text, text-to-image or image-to-image models.

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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.

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In this GitHub we fine tune a bert model from HuggingFace on review data like Yelp reviews.

The objective of this GitHub is to simulate a simple use case of Machine Learning in IRIS :
We have an IRIS Operation that, on command, can fetch data from the IRIS DataBase to train an existing model in local, then if the new model is better, the user can override the old one with the new one.
That way, every x days, if the DataBase has been extended by the users for example, you can train the model on the new data or on all the data and choose to keep or let go this new model.

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Whats NLP Stands For?

NLP stands for Natural Language Processing which is a field of Artificial Intelligence with a lot of complexity and
techniques to in short words "understand what are you talking about".

And FHIR is...???

FHIR stands for Fast Healthcare Interoperability Resources and is a standard to data structures for healthcare. There are
some good articles here explainig better how FHIR interact with Intersystems IRIS.

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Article
· Jan 16, 2020 2m read
Python Gateway VI: Jupyter Notebook

This series of articles would cover Python Gateway for InterSystems Data Platforms. Execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:

  • Execute arbitrary Python code
  • Seamlessly transfer data from InterSystems IRIS into Python
  • Build intelligent Interoperability business processes with Python Interoperability Adapter
  • Save, examine, modify and restore Python context from InterSystems IRIS

Other articles

The plan for the series so far (subject to change).

Intro

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text.

This extension allows you to browse and edit InterSystems IRIS BPL processes as jupyter notebooks.

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Challenges of real-time AI/ML computations

We will start from the examples that we faced as Data Science practice at InterSystems:

  • A “high-load” customer portal is integrated with an online recommendation system. The plan is to reconfigure promo campaigns at the level of the entire retail network (we will assume that instead of a “flat” promo campaign master there will be used a “segment-tactic” matrix). What will happen to the recommender mechanisms? What will happen to data feeds and updates into the recommender mechanisms (the volume of input data having increased 25000 times)? What will happen to recommendation rule generation setup (the need to reduce 1000 times the recommendation rule filtering threshold due to a thousandfold increase of the volume and “assortment” of the rules generated)?
  • An equipment health monitoring system uses “manual” data sample feeds. Now it is connected to a SCADA system that transmits thousands of process parameter readings each second. What will happen to the monitoring system (will it be able to handle equipment health monitoring on a second-by-second basis)? What will happen once the input data receives a new bloc of several hundreds of columns with data sensor readings recently implemented in the SCADA system (will it be necessary, and for how long, to shut down the monitoring system to integrate the new sensor data in the analysis)?
  • A complex of AI/ML mechanisms (recommendation, monitoring, forecasting) depend on each other’s results. How many man-hours will it take every month to adapt those AI/ML mechanisms’ functioning to changes in the input data? What is the overall “delay” in supporting business decision making by the AI/ML mechanisms (the refresh frequency of supporting information against the feed frequency of new input data)?

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