Machine learning (ML) is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" with data, without being explicitly programmed.
Last week, we announced the InterSystems IRIS Data Platform, our new and comprehensive platform for all your data endeavours, whether transactional, analytics or both. We've included many of the features our customers know and loved from Caché and Ensemble, but in this article we'll shed a little more light on one of the new capabilities of the platform: SQL Sharding, a powerful new feature in our scalability story.
This series of articles would cover Python Gateway for InterSystems Data Platforms. Leverage modern AI/ML tools and execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:
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.
We have more exciting news! The new InterSystems online programming contest dedicated to Generative AI, Vector Search and Machine Learning is starting very soon!
Fast transfer. Pass globals, classes and tables from InterSystems IRIS to Python with ease and speed (10x faster than old QueryExecute). Documentation.
We're pleased to invite you to the InterSystems AI+ML Summit 2021, which will be held virtually from January 25 to February 4! Join us for a two-week event that ranges from thought leadership to technical sessions and even 1:1 “Ask the Expert” sessions.
The sessions will be in both German and English. And this summit is free to attend!
In this article you will have access to the curated base of articles from the InterSystems Developer Community of the most relevant topics to learning InterSystems IRIS. Find top published articles ranked by Machine Learning, Embedded Python, JSON, API and REST Applications, Manage and Configure InterSystems Environments, Docker and Cloud, VSCode, SQL, Analytics/BI, Globals, Security, DevOps, Interoperability, Native API. Learn and Enjoy!
Apache Spark has rapidly become one of the most exciting technologies for big data analytics and machine learning. Spark is a general data processing engine created for use in clustered computing environments. Its heart is the Resilient Distributed Dataset (RDD) which represents a distributed, fault tolerant, collection of data that can be operated on in parallel across the nodes of a cluster. Spark is implemented using a combination of Java and Scala and so comes as a library that can run on any JVM.
Many factors affect a person's quality of life, and one of the most important is sleep. The quality of our sleep determines our ability to function during the day and affects our mental and physical health. Good quality sleep is critical to our overall health and well-being. Therefore, by analyzing indicators preceding sleep, we can determine the quality of our sleep. This is precisely the functionality of the Sheep's Galaxy application.
For several years now Visual Studio Code has supported the notebook coding paradigm with a maturing UX and an API that is enabling a notebook extensions ecosystem to grow. One of the best-known notebook platforms is Jupyter Notebooks. A Microsoft team publishes an extension that allows VS Code to handle .ipynb notebook files. These can either work against a local Python environment or connect to a Jupyter Server, which typically hosts remote Python environments with beefier resources.
What if your InterSystems IRIS environments, whether local on your workstation or remote in your organization / cloud, could operate as Jupyter Servers? And not only for Embedded Python but also for ObjectScript and SQL
This week is a voting week for the InterSystems AI programming contest! So, it's time to give your vote to the best solutions built with InterSystems IRIS.
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.
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
Some days ago, I've seen a youtuber talking about how to create a neural network (sorry, is in spanish)
https://www.youtube.com/embed/iX_on3VxZzk [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]
Very soon, almost every product and application will include artificial intelligence (AI).
On the afternoon of Wednesday, October 3, at the Global Summit 2018 in San Antonio we’re pulling together experts from InterSystems and from the front lines of the AI industry to discuss the current and future state-of-the-art for AI solutions.
Several years ago everyone got mad about BigData – nobody knew when smallish data will become BIGDATA, but all knows that it’s trendy and the way to go.
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.
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