Purpose:

Asymmetric cryptography is a cryptographic system that uses pairs of keys: public keys which may be disseminated widely, and private keys which are known only to the owner. The generation of such keys depends on cryptographic algorithms based on mathematical problems to produce one-way functions. Effective security only requires keeping the private key private; the public key can be openly distributed without compromising security.

In such a system, any person can encrypt a message using the receiver's public key, but that encrypted message can only be decrypted with the receiver's private key.

Robust authentication is also possible. A sender can combine a message with a private key to create a short digital signature on the message. Anyone with the sender's corresponding public key can combine the same message and the supposed digital signature associated with it to verify whether the signature was valid, i.e. made by the owner of the corresponding private key. (C) Wikipedia.

The InterSystems IRIS offer many options to deploy digital services running in IRIS. Your option will depends if you use IaC (my preferred option) or no.

If you use IaC, the options are:

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.

In most cases, a global used by default storage has just 1 subscript level that represents the IDKEY.
For an index-globals we may see 2 or more subscript levels.
Arrays, or parent-child relationships or persistent classes extending a base data class
are examples where we see more levels. Though all these globals are quite uniform.

During my search for a snapshot of a persistent object,
I met a feature that I would like to share as it could be useful in some special situations.
My trigger was to have a before- and an after-image during unit testing.

Setting up Management Portal Help Pages for Full WebServers

On each page of the System Management Portal, there is a “Help” button. This takes users to an article in documentation that describes the page functionality and use.

Caché provides local documentation for all of these articles.

InterSystems IRIS does not provide local documentation. Instead, the Help button will redirect users to the articles in the online documentation at docs.intersystems.com.

This article is a continuation of Deploying InterSystems IRIS solution on GKE Using GitHub Actions, in which, with the help of GitHub Actions pipeline, our zpm-registry was deployed in a Google Kubernetes cluster created by Terraform. In order not to repeat, we’ll take as a starting point that:

It's also an example of a customized SystemFunction extension (ZZISJSON) in Caché & IRIS
This time it is to be included in %ZLANGF00.mac

A JSON string is mostly imported from a file or over REST
You rely on a clean and compatible structure. This is the check.

required: ZPretty.mac

demo

t's also an example for a customized command extension (ZZJSON) in IRIS

IRIS has a nice %JSON.Formatter class.
But for debugging it is not really handy.
see:

ZWRITE js1  
js1="{""Name"":""Cunningham,John C."",""SSN"":""294-11-9150"",""DOB"":""1933-01-08"",""Home"":{""Street"":""4249 Ash Street"",""City"":""Tampa"",""State"":""MD"",""Zip"":""30176""},""FavoriteColors"":\[""White"",""Red"",""Green""]}"   

so you proceed for the most simple case

It is a classic Global Mapping exercise presenting ^SPOOL as Class / Table

Background

Device #2 named SPOOL dates back to the predecessors of Caché and IRIS
It was the first "%Stream" like option to buffer output before printing.
It is also the first and till today the most simple way of output redirection.

Solution

This is also an example of a mapped Global.
USE 2 redirects the output into the Global ^SPOOL

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.

IRIS and Caché / Ensemble offer the possibility to add Languages Extension as SystemFunctions, SystemVariables, SystemCommands

The related documentation tells you where to add the system-wide extension. Parameter passing is not covered explicitly.

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.

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.