This is my introduction to a series of posts explaining how to create an end-to-end Machine Learning system.

Starting with one problem

Our IRIS Development Community has several posts without tags or wrong tagged. As the posts keep growing the organization
of each tag and the experience of any community member browsing the subjects tends to decrease.

First solutions in mind

We can think some usual solutions for this scenario, like:

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Article
· Jul 19, 2020 1m read
Organize ZLANG

The guide “Extending Languages with %ZLANG Routines”
Tells you all details you need to know to extend your programming language.
EXCEPT: How to do it in a clean way.

This example of a %ZLANGC00.mac may show a possible approach
to get an easy to overview and to manage setup.
With less than 50 lines of code you might not be affected.
But if your Studio shows close to 1000 rows or more you may get in troubles.

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install and usage

Packed Pretty.xml installs routine ZPretty in any namespace.
calling $$Do^ZPretty(input,[filler],[newline]) returns a wrapped JSON string.
filler is the optional string for the indent, default = " "
newline is optional, default = $C(13,10)
input accepts: JSON_String, JSON_Stream, %DynamicAbstractObject

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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 InterSystems 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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InterSystems supports use of the InterSystems IRIS Docker images it provides on Linux only. Rather than executing containers as native processes, as on Linux platforms, Docker for Windows creates a Linux VM running under Hyper-V, the Windows virtualizer, to host containers. These additional layers add complexity that prevents InterSystems from supporting Docker for Windows at this time.

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As you probably know, InterSystems IRIS has a mechanism for auto-generating methods. So, when you create an index in a class, you create methods that make your work easier. There is also a good article on Community that describes such methods.

Take, for example, IndexNameOpen(val), where IndexName is the name of the corresponding index. This method returns an object in which the value of this index corresponds to the value of val.

But this method is available only for unique indexes.

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Let's say we have two serial classes, one as a property of another:

Class test.Serial Extends %SerialObject
{
Property Serial2 As test.Serial2;
}

Class test.Serial2 Extends %SerialObject
{
Property Property As %String;
}

And a persistent class, that has a property of test.Serial type:

Class test.Persistent Extends %Persistent
{

Property Datatype As %String;

Property Serial As test.Serial;

}

So it's a serial, inside a serial, inside a persistent object.

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Hi All,

With this article, I would like to show you how easily and dynamically System Alerting and Monitoring (or SAM for short) can be configured. The use case could be that of a fast and agile CI/CD provisioning pipeline where you want to run your unit-tests but also stress-tests and you would want to quickly be able to see if those tests are successful or how they are stressing the systems and your application (the InterSystems IRIS backend SAM API is extendable for your APM implementation).

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Imagine you want to see what InterSystems can give you in terms of data analytics. You studied the theory and now you want some practice. Fortunately, InterSystems provides a project that contains some good examples: Samples BI. Start with the README file, skipping anything associated with Docker, and go straight to the step-by-step installation. Launch a virtual instance, install IRIS there, follow the instructions for installing Samples BI, and then impress the boss with beautiful charts and tables. So far so good.

Inevitably, though, you’ll need to make changes.

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Article
· Jun 25, 2020 12m read
Backport %JSON.* to Caché

Attention:

This is a coding example working on Caché 2018.1.3
It will not be kept in sync with new versions 
It is also NOT serviced by InterSystems Support !

Full backport from IRIS for Windows (x86-64) 2020.1 (Build 215U) Mon Mar 30 2020 20:14:33 EDT

IRIS brought us an excellent %JSON.Package
It is an essential component of the Project Manager (ZPM)
This backport makes it available also in Caché and builds a base to eventually backport also ZPM.

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Article
· Jul 4, 2020 4m read
Editor Archaeology

During the development of the Terminal Multi-Line Command Editor I discovered in my IRIS installation
a piece of software that I just can classify as a historic artifact. And it is still fully operational !!!

As it dates back to times before InterSystems was founded in 1978 you may understand my surprise.
I personally stepped into that environment in 1978 and used it then for daily work.

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Article
· Jun 29, 2020 3m read
Terminal Multi-Line Option


The Command extension enables the execution of Multi-Line Commands from Terminal prompt.

Installation & Execution

Import %ZML.xml to Namespace %SYS

Optionally you may add this line to your %ZLANGC00.mac for a simplified use:

ZML do ^%zml quit  ; add multi line command in terminal

and
USER>zml

otherwise, just call it from the terminal command prompt

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Using SOAP Web Services or REST API Resources, if you want to deliver strategic digital assets for your organization, SOA aproach is an excellent option. The InterSystems IRIS supports like a charm the SOA principles with Contract First technique to model services aligned with the business, and create the services from the service contracts (Open API or WSDL).

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Now available on Open Exchange is a library of third party charts available to use within DeepSee/InterSystems IRIS BI dashboards. To start, simply download and install, select the new portlet as the widget type, then select the chart type that you desire. If you don't find the type of chart you are looking for, you can easily extend the portlet to implement your desired chart type. These new chart types can be used within existing dashboards or you can create new dashboards using them.

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In an earlier article (hope, you’ve read it), we took a look at the CircleCI deployment system, which integrates perfectly with GitHub. Why then would we want to look any further? Well, GitHub has its own CI/CD platform called GitHub Actions, which is worth exploring. With GitHub Actions, you don’t need to rely on some external, albeit cool, service.

In this article we’re going to try using GitHub Actions to deploy the server part of InterSystems Package Manager, ZPM-registry, on Google Kubernetes Engine (GKE).

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Article
· Jun 18, 2020 1m read
Debugging Business Operations

Often we need to debug a Business Operation. Tracing and logging work but sometimes you want to work with a BO as with your local terminal session.

Here's how you can do that on any operating system.

Windows has a great tool for debugging Business Operations - Foreground mode. In that mode Windows launches a local terminal with operation job.

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Keywords: PyODBC, unixODBC, IRIS, IntegratedML, Jupyter Notebook, Python 3

Purpose

A few months ago I touched on a brief note on "Python JDBC connection into IRIS", and since then I referred to it more frequently than my own scratchpad hidden deep in my PC. Hence, here comes up another 5-minute note on how to make "Python ODBC connection into IRIS".

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Hi Devs!

Last weekend I had been testing the newborn csvgen module and was looking for a CSV file to test - thus I came across an interesting datafile on Data.World with Game of Throne episodes statistics. Death statistics. These folks documented all the murders through all the 8 seasons and noted where, who, from what clan with what weapon had killed another one.

So I imported it and made an IRIS Analytics dashboard.

You Know Nothing, Jon Snow | You Know Nothing, Jon Snow | Know ...

Don't worry, Jon, with this dashboard we can figure out something ). See the details below.

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Many times it is necessary copy or send files to your docker container instance.

In my case was with IRIS JDBC driver.

Docker has this recipe for this (credits to https://docs.docker.com/engine/reference/commandline/cp/):

docker cp [OPTIONS] CONTAINER:SRC_PATH DEST_PATH|-
docker cp [OPTIONS] SRC_PATH|- CONTAINER:DEST_PATH

But to copy you need your container name. Write this command for this:

docker ps

In my, my-iris is the container name.

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