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.
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:
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:
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.
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:
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.
The Customer Churn Predictor application is a tool that demonstrates how to use IntegratedML to helps companies identify which customers are likely to leave in the near future and develop customer retention strategies. Benefits of the similar application include:
In our latest episode of Data Points, I had a conversation with @Thomas Dyarabout AI Link, which helps bridge the gap between data scientists and business analysts. Our conversation talks about how AI Link fits with IntegratedML and Adaptive Analytics, as well, as what new features are on the horizon for IntegratedML. Take a listen!
IntegratedML is a feature helps us and our teams easily implement machine learning (ML) without dedicated ML experts and data scientists. If you do not need particularly complex ML function, integratedML is a good choice and convenient that only requires executing 3 SQL queries to build predictive models directly from InterSystems IRIS to ML engine.
I've been playing with IntegratedML and have created a model and trained the model. When I try to use PREDICT or PROBABILITY statements in an SQL query, I get the following error:
[SQLCODE: <-400>:<Fatal error occurred>]
[%msg: <PREDICT execution error: ERROR #5002: ObjectScript error: <OBJECT DISPATCH>%LoadModel+31^%ML.AutoML.TrainedModel.1 *<class 'AttributeError'>: 'str' object has no attribute 'decode' - >]
Here's an example of the sequence of steps I've followed that lead to the error:
It is very interesting to compare different BI technologies. It is curious to me what the differences are in functionality, development tools, speed and usability.
Maternal Risk can be measured from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Yasir Hussein Shakir published a very useful dataset for training ML algorithms in the detection/prediction of Maternal Risk.
Kidney Disease can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Akshay Singh published a very useful dataset for training ML algorithms in the detection/prediction of kidney disease. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/akshayksingh/kidney-disease-dataset.
Diabetes can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the National Institute of Diabetes and Digestive and Kidney Diseases published a very useful dataset for training ML algorithms in the detection/prediction of diabetes. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/mathchi/diabetes-data-set.
Prediction is a critical to the Maternal healthcare. The Health Dataset Application (https://openexchange.intersystems.com/package/Health-Dataset) has 10 real health datasets to predict the most important diseases and health problems, including Maternal Risk.
This article detail the steps to predict Maternal Risk using the InterSystems IRIS IntegratedML. This is a technology of InterSystems to do predictions using SQL Commnands! Great!
Have you tried the InterSystems learning platform lab for IRIS IntegratedML? In that lab you can train and test a model on a readmission dataset and be able to predict when a patient will be readmitted or not, or calculate its probability of being readmitted.
You can try it without any installation on your system, all you have to do is start a virtual lab environment (Zeppelin) and play it around!
I'd like to know if the VALIDATE MODEL statement in IntegratedML performs a cross validation (CV) procedure internally. If it doesn't, is there a ready to use method for CV?
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