This is the second post of a series explaining how to create an end-to-end Machine Learning system.
Exploring Data
The Intersystem 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.
Following up the previous part, it's time to take advantages for IntegratedML VALIDATION MODEL statement, to provide information in order to monitor your ML models. You can watch it in action here
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.
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.
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.
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).
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.
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.
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.
The InterSystems IRIS has functions that allows create DIWK digital services. A few products have the ability to transform data into wisdom, according to the following pyramid.
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).
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.
This is the first article from a series. I will provide details, using bpmn notation, how can I do to develop, deploy, secure, operate a consume IRIS digital services, linking with IRIS documentation. Each subprocess will be described with an individual bpmn diagram. This is the macroprocess.
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).
Some InterSystems Java libraries are not available in public maven repositories, like intersystems-jdbc-3.1.0.jar. In this case, to configure your Java Maven dependency, copy the external file to your project (for a folder visible to the classpath, like resources) and use <systemPath>. Follow the sample:
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".
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.
Don't worry, Jon, with this dashboard we can figure out something ). See the details below.
Some time ago I got a WRC case transferred where a customer asks for the availability of a raw DEFLATE compression/decompression function built-in Caché.
When we talk about DEFLATE we need to talk about Zlib as well, since Zlib is the de-facto standard free compression/decompression library developed in the mid-90s.
Zlib works on particular DEFLATE compression/decompression algorithm and the idea of encapsulation within a wrapper (gzip, zlib, etc.). https://en.wikipedia.org/wiki/Zlib
Personal data privacy regulations have become an indispensable requirement for projects dealing with personal data. The compliance with these laws is based on 4 principles:
Sometimes you need quickly and easily import data into IRIS. For this, an IRIS import manager has been developed.
This application allows you to import JSON data and also provides a really simple interface for transferring data from MongoDB collections to IRIS globals. It has never been so easy.
Hi All This is the index to a series of articles I hope to create over the coming months.
ZEN and ZEN Mojo are no longer being actively developed by Intesystems - this is a great shame as it is a fine product that works so well for business applications. However ZEN is a 15 year old product and I need a path forward to replace the ZEN UI with a supported development framework.
This article is an index of the other articles I have, or plan to write. - the articles will be subject to change as I develop my thoughts and climb the learning curve.