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:

• increase the effectiveness of marketing campaigns
• improve the quality of customer service
• reduce losses associated with customer churn
• increase profit
• improve customer loyalty

In our latest episode of Data Points, I had a conversation with @Thomas Dyar about 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!

Hi Developers,

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 have recorded a video that walk through the basic of IntegratedML and implement an application from open exchange called diseases predictor by @Yuri Marx 

Check this out: Link to my video

Please feel free to leave any comments

1. Integrated ML Demonstration

This repository is a demonstration of IntegratedML and Embedded Python.

• 1. Integrated ML Demonstration
• 2. Building the demo
  • 2.1. Architecture
  • 2.2. Building the nginx container
• 3. Running the demo
• 4. Python back-end
  • 4.1. Embedded Python
    • 4.1.1. Setting up the container
    • 4.1.2. Using Embedded Python
    • 4.1.3. Side By Side Comparaison
  • 4.2. Launching the server
• 5. IntegratedML
  • 5.1. Exploring both datasets
  • 5.2. Managing models
    • 5.2.1. Creating a model
    • 5.2.2. Training a model
    • 5.2.3. Validating a model
    • 5.2.4. Making predictions
• 6. Using COS
• 7. More explainability with DataRobot
• 8. Conclusion

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:

CREATE MODEL MyModel PREDICTING (IsError BOOLEAN) FROM Example3.ModelTable

Hey Community, 

Don't miss the latest videos on InterSystems Developers YouTube channel:

⏯ IntegratedML Update from the Field

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.

For this application, I chose a dataset with water conditions in various European countries. This is an open source dataset containing observational data from 1991 to 2017.

The team and I decided to make a model based on this BI dataset using IRIS BI, Tableau, PowerBI and InterSystems Reports (powered by Logi Reports).

For the frontend, we made a web interface in PythonFlask via Embedded Python.

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. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/code/yasserhessein/classification-maternal-healt….

About the Dataset

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.

About the 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.

Is it possible train an IntegratedML model on docker build or docker up?

The InterSystems IRIS IntegratedML feature is used to get predictions and probabilities using the AutoML technique. The AutoML is a Machine Learning technology used to select the better Machine Learning algorithm/model to predict status, numbers and general results based in the past data (data used to train the AutoML model). You don't need a Data Scientist, because the AutoML it will test the most common Machine Learning algorithms and select the better algorithm to you, based in the data features analysed. See more here, in this article.

Hi community,

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!

Follow these steps:

1. Clone/git pull the repo into any local directory

$ git clone https://github.com/yurimarx/predict-maternal-risk.git

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!

Hi Community,

Please welcome the new video from #VSummit20:

⏯ Showcasing Health Insight & IntegratedML for Healthcare Applications

Hi Community,

We're pleased to invite you to the upcoming webinar in Spanish called "IntegratedML - Creating ML models in minutes"!

Date & Time: September 30, 4:00 PM (CEST)

Speaker: @Eduardo Anglada, Sales Engineer, InterSystems Iberia

Hi!

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?

Thanks!

Talking with a friend of mine, Machine Learning specialist @Renato Banzai , he brought one of the biggest challenges faced by companies nowadays: deploying ML/AI in live environments.

Hi Community,

Please welcome the new video on InterSystems Developers YouTube:

⏯ Machine Learning with IntegratedML & Data Robot

Hi!

I'd like to know how to get feature importance after creating and training a model, using IntegratedML with AutoML provider?

Thanks!

Hi Community,

Please welcome the new video on InterSystems Developers YouTube:

⏯ Getting Started with IntegratedML

Now Sapphire enable you load CSV to IRIS. See the steps:

1) Create a sample CSV file using Excel (save file as CSV):

2) Follow these instructions to install Sapphire into your enviroment: https://openexchange.intersystems.com/package/SAPPHIRE

3) Access Sapphire web page. Go to top menu Import > Load CSV

4) Configure access to your IRIS target instance, select new table, set your new table name, click Choose button and load your csv file and click upload. Click Get Definitions.

GA releases are now published for the 2020.3 version of InterSystems IRIS, IRIS for Health, with IntegratedML!

Update: added support for regression model Hi everyone!

In this brief article, I'll show you how to write an adapter for IRIS Interoperability for use ML models managed by IRIS IntegratedML.

The adapter

Hi Community,

See how IntegratedML can leverage DataRobot — a best-in-class, third-party machine learning platform — to make predictions on a set of data:

⏯ Using IntegratedML with DataRobot

Hi Community,

See how IntegratedML in InterSystems IRIS can be used to make machine learning predictions directly from SQL commands:

⏯ IntegratedML in a Nutshell

Keywords:  IRIS, IntegratedML, Flask, FastAPI, Tensorflow Serving, HAProxy, Docker, Covid-19

Purpose:

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

Purpose

Recently I noticed a Kaggle dataset  for the prediction of whether a Covid-19 patient will be admitted to ICU.  It is a spreadsheet of 1925 encounter records of 231 columns of vital signs and observations, with the last column of "ICU" being 1 for Yes or 0 for No. The task is to predict whether a patient will be admitted to ICU based on known data.

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. 

IntegratedML Approach?

How to load data into IRIS

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.