The last time that I created a playground for experimenting with machine learning using Apache Spark and an InterSystems data platform,  see Machine Learning with Spark and Caché, I installed and configured everything directly on my laptop: Caché, Python, Apache Spark, Java, some Hadoop libraries, to name a few. It required some effort, but eventually it worked. Paradise. But, I worried. Would I ever be able to reproduce all those steps? Maybe. Would it be possible for a random Windows or Java update to wreck the whole thing in an instant? Almost certainly.

This series of articles would cover Python Gateway for InterSystems Data Platforms. Execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:

• Execute arbitrary Python code
• Seamlessly transfer data from InterSystems IRIS into Python
• Build intelligent Interoperability business processes with Python Interoperability Adapter
• Save, examine, modify and restore Python context from InterSystems IRIS

Other articles

The plan for the series so far (subject to change)

The titular question was quite relevant and often discussed some thirty years ago. The thought went: “Sure, there are industries where computers are the norm, but in my industry we got just fine so far, the benefits are questionable, problems innumerable and unsolved. Can we continue as before or should we embrace this new technology?”

Today, everyone asks the same question but about Machine Learning and Artificial Intelligence. The doubts are the same – lack of expertise, lack of known path, perceived irrelevancy to the industry.

Yet, as before, the correct, even the only possible answer is a resounding yes. Read on to find out why.

Keywords:   Jupyter Notebook, Tensorflow GPU, Keras, Deep Learning, MLP,  and HealthShare    

1. Purpose and Objectives

In  previous"Part I" we have set up a deep learning demo environment. In this "Part II" we will test what we could do with it.

Many people at my age had started with the classic MLP (Multi-Layer Perceptron) model. It is intuitive hence conceptually easier to start with.

So let's try a Keras "deep learning MLP" with standard demo data that everybody in AI/NN community has been using. It is a kind of so called "supervised learning". We will see how simple to run it on the Keras level.

We could later touch on its history and on why it's called "deep learning" the buzz word - what actually evolved over the recent 20 years.�

Hi Community!

Please welcome a new video on Developer Community YouTube Channel:

Alexa: Connect Me with the World of IoT

Hi all.

I want to insert my dataframe into InterSystems IRIS. So, I tried to do this:

df = spark.read.load("/home/imported-openssh-key/zeppelin-0.8.0-bin-all/bin/resultData3/DF.json", format="json")
df.write.format("com.intersystems.spark").\
option("url", "IRIS://localhost:51773/DEDUPL").\
option("user", "********").option("password", "********").\
option("dbtable", "try.test1").save()

And got this error

View Machine Learning 101 recording at: https://videos.intersystems.com/detail/video/5827774460001/machine-learning-101?autoStart=true&q=machine%20learning

Hi Everyone!

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. 

Learn more about our Post-Summit Symposium: Artificial Intelligence and Machine Learning.

The recording of Anton Umnikov's It's Machine Learning, Not Rocket Science! webinar is now available on learning.intersystems.com. 

In it, Anton provides a great, high-level introduction to machine learning and shows why you don't need to be a "unicorn" data scientist to start using machine learning to your advantage! 

Watch it now

Hi Everybody!

We are pleased to invite you to the upcoming live webinar "It’s Machine Learning, Not Rocket Science!" on 19th of July at 11:00 a.m. EDT!

Hi all. Yesterday I tried to connect Apache Spark, Apache Zeppelin, and InterSystems IRIS. During the process, I experienced troubles connecting it all together and I did not find a useful guide. So, I decided to write my own.

Introduction

What is Apache Spark and Apache Zeppelin and find out how it works together. Apache Spark is an open-source cluster-computing framework. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. So, it is very useful when you need to work with Big Data. And Apache Zeppelin is a notebook, that provides cool UI to work with analytics and machine learning. Together, it works like this: IRIS provides data, Spark reads provided data, and in a notebook we work with the data.

Note: I have done the following on Windows 10

With the release of InterSystems IRIS, we're also making available a nifty bit of software that allows you to get the best out of your InterSystems IRIS cluster when working with Apache Spark for data processing, machine learning and other data-heavy fun. Let's take a closer look at how we're making your life as a Data Scientist easier, as you're probably already facing tough big data challenges already, just from the influx of job offers in your inbox!

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.