InterSystems IntegratedML is an all-SQL machine learning (ML) module for InterSystems IRIS or IRIS for Health that: - 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.
If you are a customer of the new InterSystems IRIS® Cloud SQL and InterSystems IRIS® Cloud IntegratedML® cloud offerings and want access to the metrics of your deployments and send them to your own Observability platform, here is a quick and dirty way to get it done by sending the metrics to Google Cloud Platform Monitoring (formerly StackDriver).
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.
InterSystems has been at the forefront of database technology since its inception, pioneering innovations that consistently outperform competitors like Oracle, IBM, and Microsoft.
Dear community, we are building up a digital backbone for our 17 hospitals and looking for reinforcement within the team. Maybe this is something for you or someone you know?
Some days ago, I've seen a youtuber talking about how to create a neural network (sorry, is in spanish)
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This document mainly enriches the content of the previous article and introduces the use of the application.
Perhaps you have already read the previous article, but I still want to say, After completing the initialization operation (including model creation and training), the Fhir HepatitisC Predict application then predicts HepatitisC
Processing FHIR resources with FHIR SQL BUILDER to predict the probability of developing hepatitis C disease
With the development of technology, the medical industry is also constantly advancing, and humans often pay more attention to their own health, By learning and processing datasets through computers, diseases can be predicted.
Pre condition: Ability to use FHIR and ML Firstly, our dataset is obtained from kaggle and transformed into FHIR resources based on patient gender, age, ALP or ALT, and imported into the FHIR resource repository
InterSystems is pleased to announce the General Availability of InterSystems IRIS Cloud SQL and InterSystems IRIS Cloud IntegratedML, two foundational services for developing cloud-native solutions powered by the proven, enterprise-class performance and reliability of InterSystems IRIS technology.
It's true! QuinielaML has incorporated the most important leagues in Europe (and Brazil) into its prediction service, so, dear members of the Developer Community, wherever you are from, you will be able to have the predictions of your favorite leagues at your disposal.
From the predictions screen you will have access to each of the new leagues included, being able to record the matches for each journey:
Taking advantage of the Quiniela ML application and as we indicated in the previous article, we are going to explain how we can perform a JWT authentication between our frontend developed in Angular and our backend developed in InterSystems IRIS.
I remind you of the architecture of our QuinielaML project:
Interested in predictive modeling? This exercise shows how IntegratedML® works, and you can get practice creating a model with sample patient data. Try it out and share what you think!
Recently @Anastasia Dyubaylopublished a post (this one) showing a new IntegratedML functionality for time series predictions that @Thomas Dyaralready presented to us at the Global Summit 2023 so, let's go to set up a small workshop to test
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We continue with the series of articles related to the QuinielaML application. In this article we are going to cover how to prepare the raw data that we have captured using the Embedded Python functionality.
Watch this video to see how you can use IntegratedML time-aware modeling in InterSystems IRIS to make predictions for data values based on historical trends, and how this differs from traditional IntegratedML modeling:
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Welcome dear members of the Community to the presentation and first article of a small project that will demonstrate the capabilities of InterSystems IRIS to provide full backup functionality for a web application developed in Angular. This article will be limited to presenting the concept as well as the InterSystems IRIS functionalities used in a general way, going into more detail in subsequent articles.
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Watch this video to learn how to build and run machine-learning models using InterSystems IRIS Cloud IntegratedML using the example of 15,000 rows of data from New York City taxi rides to predict the tip for a given ride:
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A simple data analysis example created in IntegratedML and Dashboard
Based on InterSystems' Integrated ML technology and Dashboard, automatically generate relevant predictions and BI pages based on uploaded CSV files. The front and back ends are completed in Vue and Iris, allowing users to generate their desired data prediction and analysis pages with simple operations and make decisions based on them.
As you know, if you regularly read the articles that are published in the Community, last May InterSystems organized the JOnTheBeach2023 Hackathon held in Malaga (Spain). The topic that was proposed was the use of predictive analysis tools that InterSystems IRIS makes available to all developers with IntegratedML.
The platform server entry configuration prediction application connects to Iris in Java and uses its Integrated ML technology to analyze data such as hospital outpatient volume, number of services, number of messages, and message save time. It can predict the server configuration required for the hospital entry platform before the hospital integration platform enters, providing convenience for customers.
InterSystems IRIS Cloud SQL is a fully managed cloud service that brings the power of InterSystems IRIS relational database capabilities used by thousands of enterprise customers to a broad audience of application developers and data professionals. InterSystems IRIS Cloud IntegratedML is an option to this database-as-a-service that offers easy access to powerful Automated Machine Learning capabilities in a SQL-native form, through a set of simple SQL commands that can easily be embedded in application code to augment them with ML models that run close to the data.
Today, we announce the Developer Access Program for these two offerings. Application developers can now self-register for the service, create deployments and start building composable applications and smart data services, with all provisioning, configuration and administration taken care of by the service.
In recent years, artificial intelligence technologies for text generation have developed significantly. For example, text generation models based on neural networks can produce texts that are almost indistinguishable from texts written by humans. ChatGPT is one such service. It is a huge neural network trained on a large number of texts, which can generate texts on various topics and be matched to a given context.
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