With the world (as well as our own technology) moving to the cloud at such a fast pace it is easy (at least for myself) to get caught up in the little details. One thing I, and some clients of ours, had run into a couple of times was the necessity to specify the version of the images one plans to use with the IKO.
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.
In the first one, I have defined a class which Extends (%Persistent, Ens.Util.MessageBodyMethods), we'll call it NSOne.Msg.Req
In the second namespace NSTwo, I want to use the previous class with something like SET pInput = ##class(NSOne.Msg.Req).%New()
I mapped the NSOne.Msg.Req package in namespace NSOne. In Atelier, I can see NSOne.Msg.Req in my NSOne. But, when I try to execute line 2 above, it tells me :
How Tax Service, OpenStreetMap, and InterSystems IRIS could help developers get clean addresses
Pieter Brueghel the Younger, Paying the Tax (The Tax Collector), 1640
In my previous article, we just skimmed the surface of objects. Let's continue our reconnaissance. Today's topic is a tough one. It's not quite BIG DATA, but it's still the data not easy to work with: we're talking about fairly large amounts of data. It won't all fit into RAM at once, and some of it won't even fit on the drive (not due to lack of space, but because there's a lot of junk). The name of our subject is FIAS DB: the Federal Information Address System database - the databases of addresses in Russia. The archive is 5.5 GB. And it's a compressed XML file. After extraction, it will be a full 53 GB (set aside 110 GB for extraction). And when you start to parse and convert it, that 110 GB won't be enough. There won't be enough RAM either.
Running predictive models natively in an InterSystems IRIS Business Process has of course always been the goal of our PMML support, but somehow never made it into the kit because there were a few dependencies and choices that needed addressing and answering. Anyhow, thanks to some pushing and code kindly provided by @Amir Samary (Thanks again Amir!), we finally got it wrapped in a GitHub repo for your enjoyment, review and suggestions.
Hi world, i want to configure my ensemble production that each 24 houres a business service is created and trig a business process wich browse my sql table and generate alert if there is not a new record in interval (24 HOURES).
I have been following the online Zen Quickstart Tutorial using the lastest release documentation. In addition to playing around with the styling and making a few minor functionality tweaks, I wanted to add an additional column that shows a count of the number of phone numbers for that Contact (as shown in the image below).
We need to add some stored data to an ACK message being sent back to the originating system. I have been looking at how to adapt the standard "EnsLib.HL7.MsgRouter.RoutingEngine" to do this as I cannot see any obvious way. Any suggestion?
As you have seen in the latest community publications, InterSystems IRIS has included since version 2024.1 the possibility of including vector data types in its database and based on this type of data vector searches have been implemented. Well, these new features reminded me of the article I published a while ago that was based on facial recognition using Embedded Python.
In this article we are going to demonstrate the great potential that IRIS/HealthConnect makes available to all its users with the use of Embedded Python and we are going to do it by developing a small production that will allow us to recognize and identify the faces present in a JPG file from some images that we will use as a reference.
We're upgrading to IRIS 2020.1 from Ensemble 2018.x.
I have a lookup table class that compiles fine in Ensemble but in IRIS causes the following compilation error: "ERROR #9101: Global name 'HH.LookupLabResultsToPhysiciansD' for 'IDLocation' is too long, must be no more than 31 characters in length."
Is this length limitation a new restriction or could I have done something years ago to increase the maximum character length.
I'm being asked if I can set up a AWS EC2 containerised version of IRIS and a separate AWS EC2 installation of Apache to serve IRIS web files.
Creating the Apache EC2 and containerised IRIS EC2 is the simple bit but getting them both to talk to each other does not seem quite so simple and I cannot find any documentation to describe how this can be achieved.
With access to InterSystems unified data platform on all three major cloud providers, developers and customers have flexibility to rapidly build and scale the digital applications driving the future of care on the platform of their choice.
I need advice converting a comma delimited string container with multiple records into some type of recordmap that iterates through all the records.
My string container has several records and I would like to loop through the number of records in the string container and transform each record in the container individually. Number of records will vary but the number of fields per record is static (28 fields). Meaning after every 28 fields, a new record begins. The goal is to convert to individual delimited flat file records.
In the previous article. Practices of class members and their execution within embedded Python. We will now turn our attention to the process of switching namespaces, accessing global variables , traversing and routine executions within embedded Python.