#Machine Learning (ML)

Hi Community,

As you may know, this year we're holding InterSystems AI+ML Summit 2021 virtually from January 25 to February 4. All sessions, both in German and English, will be free to attend.

Let's find out more about the exciting sessions that await us and book a virtual seat today!

Check the agenda below:

Hi Community,

We're pleased to invite you to the online meetup with the winners of the InterSystems Analytics Contest!

Date & Time: Monday, January 4, 2021 – 10:00 EDT

What awaits you at this virtual Meetup?

• Our winners' bios.
• Short demos on their applications.
• An open discussion about technologies being used, bonuses, questions. Plans for the next contests.

Hey Developers,

This week is a voting week for the InterSystems Analytics Contest! So, it's time to give your vote to the best solutions built with InterSystems IRIS.

🔥 You decide: VOTING IS HERE 🔥

How to vote?

Please meet the new voting engine and algorithm for the Experts and Community nomination:

Hi Community!

We are pleased to invite all the developers to the upcoming InterSystems Analytics Contest Kick-off Webinar! The topic of this webinar is dedicated to the Analytics contest.

On this webinar, we’ll demo the iris-analytics-template and answer the questions on how to develop, build, and deploy Analytics applications using InterSystems IRIS.

Date & Time: Monday, December 7 — 12:00 PM EDT

Speakers:
🗣 @Carmen Logue, InterSystems Product Manager - Analytics and AI
🗣 @Evgeny Shvarov, InterSystems Developer Ecosystem Manager

Keywords: Python, JDBC, SQL, IRIS, Jupyter Notebook, Pandas, Numpy, and Machine Learning

1. Purpose

This is another 5-minute simple note on invoking the IRIS JDBC driver via Python 3 within i.e. a Jupyter Notebook, to read from and write data into an IRIS database instance via SQL syntax, for demo purpose.

Hi Community,

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

⏯ IntegratedML in a Nutshell

IRIS is an excellent option for machine learning projects with massive data operation scenarios, because the following reasons:

Hi Community!

Enjoy watching the new video on InterSystems Developers YouTube:

⏯ Big Data in InterSystems IRIS

https://www.youtube.com/embed/1X-O7-QWBsk
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

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.

IRIS is an excellent option for machine learning projects with massive data operation scenarios, because the following reasons:

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

Training a Machine Learning Model

When you work with machine learning is common to hear this work: training. Do you what training mean in a ML Pipeline?
Training could mean all the development process of a machine learning model OR the specific point in all development process
that uses training data and results in a machine learning model.

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:

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.