Hey Developers! 

Join our next competition in creating open-source solutions using InterSystems IRIS Data Platform! Please welcome:

➡️ InterSystems IRIS AI Programming Contest ⬅️

Duration: June 29 – July 19, 2020. 

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

Purpose:

We touched on some quick demos of  deep learning and machine learning over the past few months, including a simple Covid-19 X-Ray image classifier and a Covid-19  lab result classifier for possible ICU admissions.  We also touched on an IntegratedML demo implementation of the ICU classifier.

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.

This dataset seems to be a good example of what's called "traditional ML" task. The data seem to have the right quantity and relatively right quality.

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.

Hey Developers,

We're pleased to invite you to the "Best practices of in-platform AI/ML" webinar by InterSystems on April 28th at 11:00 EST/17:00 CET.

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

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!

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.

The build number for these releases is: 2020.3.0AA.331.0

Full product installation kits, container images, and evaluation license keys are available via the WRC's preview download site.

Community Edition containers can also be pulled from the Docker store using the following commands:

• docker pull store/intersystems/iris-aa-community:2020.3.0AA.331.

Hi Developers!

The InterSystems IRIS AI Contest is over. Thank you all for participating in our IRIS AI Competition!

And now it's time to announce the winners! 

A storm of applause goes to these developers and their applications:

InterSystems has been at the forefront of database technology since its inception, pioneering innovations that consistently outperform competitors like Oracle, IBM, and Microsoft. By focusing on an efficient kernel design and embracing a no-compromise approach to data performance, InterSystems has carved out a niche in mission-critical applications, ensuring reliability, speed, and scalability.

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.

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.

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:

Usually it is a cumbersome process in web applications to develop the administration and management of user access, but in our case InterSystems IRIS simplifies the process by providing us with all the infrastructure we need.

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

integredML-demo-template

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.

Welcome to QuinielaML!

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

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

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.

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. 

A new task for people is to develop ways to recognize texts written not only by people but also by artificial intelligence (AI).

Hi colleagues!

InterSystems Grand Prix 2023 unites all the key features of InterSystems IRIS Data Platform!

Thus we invite you to use the following features and collect additional technical bonuses that will help you to win the prize!

Here we go!

• LLM AI or LangChain usage: Chat GPT, Bard and others - 6
• InterSystems FHIR SQL Builder- 5
• InterSystems FHIR - 3
• IntegratedML - 4
• Native API - 3
• Embedded Python - 4
• Interoperability - 3
• Production EXtension(PEX) - 2
• Adaptive Analytics (AtScale) Cubes usage - 3
• Tableau, PowerBI, Logi usage - 3
• InterSystems IRIS BI - 3 
• Columnar Index Usage - 1
• Docker container usage - 2 
• ZPM Package deployment - 2
• Online Demo - 2
• Unit Testing - 2
• Implement InterSystems Community Idea - 4
• First Article on Developer Community - 2
• Second Article On DC - 1
• Code Quality pass - 1
• First Time Contribution - 3
• Video on YouTube - 3

Recently @Anastasia.Dyubaylopublished a post (this one) showing a new IntegratedML functionality for time series predictions that @tomdalready presented to us at the Global Summit 2023 so, let's go to set up a small workshop to test it!

Introduction

For this workshop we have chosen as the topic the prediction of monthly users of the Valencia Metro line by line. To do this, we have monthly data broken down by lines since 2022 as well as annual data by lines since 2017 that we will extrapolate monthly.

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.

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.

I simulated a simple performance issue scenario in an Open Exchange technology example application - iris-integratedml-monitor-example, which is competing in the InterSystems IRIS AI Contest. Please, after read this article, you can check it out and, if you like it, vote for me

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

This is the first InterSystems IRIS release that includes IntegratedML, a new feature that brings "best of breed" machine learning to analysts and developers via simple and intuitive SQL syntax. Developers can now easily train and deploy powerful predictive models from within IRIS, right where their data lives. Documentation for IntegratedML is available as a User Guide. Virtual Summit 2020 features a number of sessions and an Experience Lab featuring IntegratedML, see overview here

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. We must thank both @tomdand @Dmitry.Maslennikovfor all the work and effort they put into making it a resounding success.

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 IntegratedML^TM 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.

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.

The diabetes dataset has the following metadata information (source: https://www.kaggle.

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.

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.

Hi Community,

Please welcome the new video on InterSystems Developers YouTube:

⏯ Getting Started with IntegratedML

When trying to create a ML Model I ran into the problem, that AutoML apparantly isn't available on my IRIS instance.

I was able to use the following command successfully: CREATE MODEL ECLASSPREDICT PREDICTING (eClass) FROM SQLUSER.CRMSHOPARTIKEL

Then when trying to use TRAIN MODEL ECLASSPREDICT, I get the error from the image:

Roughly translates to : "-186: Modelprovider not available on this instance. #2822 %ML provider 'AutoML' is not available on this instance"

In my ML Configurations there are 3 standard configurations called %AutoML, %H20 and %PMML which I haven't changed.