Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.
We’re looking for Python developers to participate in our Embedded Python Early Access Program! If you (or someone you know) are a Python developer and are interested, please contact us via the email address below.
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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ML Toolkit for InterSystems IRIS, besides Python/R/Julia, allows orchestrating cloud-based advanced analytics services, such as Microsoft Azure Data Factory and Machine Learning:
Hello everyone, this is with great pleasure that I announce the V2 of my application 'Contest-FHIR'.
In this new version, I used new tools and techniques I discovered at the EUROPEAN HEALTHCARE HACKATHON in which I was invited by InterSystems as a guest and as a mentor to display the multiple projects I did in my intership back in April 2022.
Today I present to you the V2 of my application, it can now transform CSV to FHIR to SQL to JUPYTER notebook.
With the advent of Embedded Python, a myriad of use cases are now possible from within IRIS directly using Python libraries for more complex operations. One such operation is the use of natural language processing tools such as textual similarity comparison.
We all know that having a set of proper test data before deploying an application to production is crucial for ensuring its reliability and performance. It allows to simulate real-world scenarios and identify potential issues or bugs before they impact end-users. Moreover, testing with representative data sets allows to optimize performance, identify bottlenecks, and fine-tune algorithms or processes as needed. Ultimately, having a comprehensive set of test data helps to deliver a higher quality product, reducing the likelihood of post-production issues and enhancing the overall user experience.
In this article, let's look at how one can use generative AI, namely Gemini by Google, to generate (hopefully) meaningful data for the properties of multiple objects. To do this, I will use the RESTful service to generate data in a JSON format and then use the received data to create objects.
This is my introduction to a series of posts explaining how to create an end-to-end Machine Learning system.
Starting with one problem
Our IRIS Development Community has several posts without tags or wrong tagged. As the posts keep growing the organization
of each tag and the experience of any community member browsing the subjects tends to decrease.
First solutions in mind
We can think some usual solutions for this scenario, like:
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As you may know, this year we're holding InterSystems AI+ML Summit 2021 virtually from January 25 to February 4. All sessions, both in German and English, will be free to attend.
Let's find out more about the exciting sessions that await us and book a virtual seat today!
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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Hi Community, we would like to share the news about the "Bell State" example (a compact quantum computing program) automated using our ML Toolkit with a Rigetti QVM running in the background:
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