Eduard Lebedyuk · 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).


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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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.

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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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Hi Everyone!

Very soon, almost every product and application will include artificial intelligence (AI).

On the afternoon of Wednesday, October 3, at the Global Summit 2018 in San Antonio we’re pulling together experts from InterSystems and from the front lines of the AI industry to discuss the current and future state-of-the-art for AI solutions.

Learn more about our Post-Summit Symposium: Artificial Intelligence and Machine Learning.

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Hey Community,

We're pleased to invite you to the InterSystems AI+ML Summit 2021, which will be held virtually from January 25 to February 4! Join us for a two-week event that ranges from thought leadership to technical sessions and even 1:1 “Ask the Expert” sessions.

The sessions will be in both German and English. And this summit is free to attend!

See details below:

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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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Hi Community!

We are pleased to invite all the developers to the upcoming InterSystems AI Programming Contest Kick-Off Webinar! The topic of this webinar is dedicated to the InterSystems IRIS AI Programming Contest.

On this webinar, we will talk and demo how to use IntegratedML and PythonGateway to build AI solutions using InterSystems IRIS.

Date & Time: Monday, June 29 — 11:00 AM EDT

🗣 @Thomas Dyar, Product Specialist - Machine Learning, InterSystems
🗣 @Eduard Lebedyuk, Sales Engineer, InterSystems

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InterSystems IRIS ML Toolkit adds the power of InterSystems IntegratedML to further extend convergent scenario coverage into the area of automated feature and model type/parameter selection. The previous "manual" pipelines now collaborate within the same analytic process with "auto" pipelines that are based on automation frameworks, such as H2O.

Automated classification modeling in InterSystems IRIS ML Toolkit

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Keywords: IRIS, IntegratedML, Machine Learning, Covid-19, Kaggle

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.

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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:

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Eduard Lebedyuk · Sep 12, 2019
Python Gateway 0.8 release

I'm happy to announce the latest Python Gateway release.

This is not an InterSystems product, it is community supported open source project.

Download new release from GitHub.

Now for the new features.

Fast transfer. Pass globals, classes and tables from InterSystems IRIS to Python with ease and speed (10x faster than old QueryExecute). Documentation.

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What is Distributed Artificial Intelligence (DAI)?

Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).

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Currently, the process of using machine learning is difficult and requires excessive consumption of data scientist services. AutoML technology was created to assist organizations in reducing this complexity and the dependence on specialized ML personnel.

AutoML allows the user to point to a data set, select the subject of interest (feature) and set the variables that affect the subject (labels). From there, the user informs the model name and then creates his predictive or data classification model based on machine learning.

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Hi Community!

We're pleased to invite you to the Online Meetup with the Winners of the InterSystems IRIS AI Programming Contest!

Date & Time: Friday, July 24, 2020 – 11:00 EDT

What awaits you at this virtual Meetup?

  • Our winners' bios.
  • Short demos on their applications.
  • A short interview with all the winners about the past contest. Plans for the next contests.

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A few months ago, I read this interesting article from MIT Technology Review, explaing how COVID-19 pandemic are issuing challenges to IT teams worldwide regarding their machine learning (ML) systems.

Such article inspire me to think about how to deal with performance issues after a ML model was deployed.

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Hi Community,

We're pleased to invite you to join the upcoming InterSystems IRIS 2020.1 Tech Talk: Data Science, ML & Analytics on April 21st at 10:00 AM EDT!

In this first installment of InterSystems IRIS 2020.1 Tech Talks, we put the spotlight on data science, machine learning (ML), and analytics. InterSystems IntegratedMLTM brings automated machine learning to SQL developers. We'll show you how this technology supports feature engineering and chooses the most appropriate ML model for your data, all from the comfort of a SQL interface. We'll also talk about what's new in our open analytics offerings. Finally, we'll share some big news about InterSystems Reports, our "pixel-perfect" reporting option. See how you can now generate beautiful reports and export to PDF, Excel, or HTML.

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