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

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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.

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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. 

 

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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.

 

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Hey Developers!

This week is a voting week for the InterSystems IRIS AI Programming Contest!

So, it's time to give your vote to the best AI- and ML-enabled solution on InterSystems IRIS!

🔥 You decide: VOTING IS HERE 🔥

 

How to vote? This is easy: you will have one vote, and your vote goes either in Experts Nomination or in Community Nomination.

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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.

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This is the second post of a series explaining how to create an end-to-end Machine Learning system.

Exploring Data

The Intersystem IRIS already has what we need to explore the data: an SQL Engine! For people who used to explore data in
csv or text files this could help to accelerate this step. Basically we explore all the data to understand the intersection
(joins) which should help to create a dataset prepared to be used by a machine learning algorithm.

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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.

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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

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