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#Machine Learning

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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.

Hi developers!

We are starting InterSystems AI Programming Contest next week, and according to the rules, you can include some technology IRIS Features into your solutions, which will give you extra points in the voting.

Here are the technology bonuses for InterSystems AI Programming Contest!

1. IntegratedML usage - 2 expert vote points

IntegratedML is a new technology Introduced in InterSystems IRIS which you can use with InterSystems IRIS 2020.2 Advanced Analytics Preview release. IntegratedML:

  • 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.

Learn more in IntegratedML Resource Guide.

You can use with IntegratedML template.

2. Python Gateway usage - 1 expert vote point

Python Gateway is an addon to InterSystems IRIS which gives you the way to use Python in 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.

Learn more about Python Gateway.

You can use the Python Gateway template, which includes IntegratedML too.

3. Docker container - 1 expert vote point

The application gets a 'Docker container' bonus if it uses InterSystems IRIS running in a docker container.

Both templates, IntegratedML template and Python Gateway template use docker so you can collect this bonus if you build your solution using these templates.

Or you can use any other Docker-based templates, published on Open Exchange.

Feel free to ask any questions about using the listed technologies.

Good luck in the competition!

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Keywords: Anaconda, Jupyter Notebook, Tensorflow GPU, Deep Learning, Python 3 and HealthShare

1. Purpose and Objectives

This "Part I" is a quick record on how to set up a "simple" but popular deep learning demo environment step-by-step with a Python 3 binding to a HealthShare 2017.2.1 instance . I used a Win10 laptop at hand, but the approach works the same on MacOS and Linux.

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Hey Developers,

Good news! One more upcoming in-person event is nearby.

We're pleased to invite you to join "J On The Beach", an international rendezvous for developers and DevOps around Big Data technologies. A fun conference to learn and share the latest experiences, tips & tricks related to Big Data technologies, and, the most important part, it’s On The Beach!

🗓 April 27-29, 2022

📍Málaga, Spain

This year, InterSystems is a Gold Sponsor of the JOTB.

We're more than happy to invite you and your colleagues to our InterSystems booth for a personal conversation. As always, there will be some surprises on it... 😁

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You may have seen earlier this week that we launched a brand-new learning podcast called Data Points! There are three episodes released, one of which was a really interesting discussion with Thomas Dyar — a product specialist here at InterSystems focused on machine learning. Take a listen and reach out if you're interested in exploring more about IntegratedML!

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Hi Developers!

Here're the technology bonuses for the InterSystems IRIS AI contest that will give you extra points in the voting.

IntegratedML usage - 4 points

Use InterSystems IntegratedML in you AI/ML solution. Here is the template that uses it.

Be sure that the IRIS version is not less than 2021. The latest ML images with ZPM are:

intersystemsdc/iris-ml-community:2021.1.0.215.0-zpm
intersystemsdc/irishealth-ml-community:2021.1.0.215.0-zpm

R Gateway and Python gateway usage - 4 points

InterSystems IRIS 2021 release contains two new features - R gateway and Python gateway. Here is the template on how to use the R gateway. Here is a short demo of how to use it.

Embedded Python usage - 4 points

Embedded Python is a very new feature of InterSystems IRIS that gives you the option to use python as a "first-class citizen" in backend business logic development with InterSystems classes. Short demo of Embedded Python.

Embedded python could be used in "on-demand" images that could be delivered via InterSystems Early Access Program (EAP) if you refer to python-interest@intersystems.com.

Here is the template package on how to use Embedded Python deployable with ZPM. Don't forget to change the image to the one you get from the Early Access program.

PMML usage - 4 points

PMML - Predictive Modelling Markup Language - can be used to build AI/ML solutions with InterSystems IRIS. Check with documentation.

There is an example in Open Exchange on how to use PMML.

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Hi everyone.
We are a team of company "Constructor" and we develop cutting edge cartographic systems. Recently the amount of image data skyrocketed so we want to give our users the ability to tie images to places automatically. For that, we want to use AI/ML technologies and we have a cool task for you.

https://cloud.mail.ru/public/pHbC/4r7Z58m6f/

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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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