At READY 2026, for the very first time, we held a customer hackathon — and it was an amazing experience!
The hackathon took place on the pre-event day at 9 am. Some participants, like me, arrived early due to jet lag—but that actually helped us connect with fellow hackers before the event kicked off.
 

The Beginning

This article presents a straightforward approach to automatically and efficiently tune hyperparameters for machine learning models using Optuna as the optimisation framework. We explore how to use both Optuna’s native storage options and InterSystems IRIS as a database backend to track the progress of hyperparameter searches. We also show how MLflow can be used to monitor experiments and manage models through its tracking and model registry UI.

This article is based on this Kaggle Notebook, which you can run and directly edit yourself.

Key Responsibilities & Requirements

Core Infrastructure & Cloud Management

• Full-Stack Administration: Maintain and optimize both Linux (Ubuntu/RHEL) and Windows Server environments.
• Cloud Orchestration: Take full ownership of VM cloud infrastructure, including provisioning, health monitoring, backup and resource scaling.
• Policy Governance: Design and enforce comprehensive SysAdmin and Deployment policies (CI/CD pipelines, automated provisioning, and security hardening).

InterSystems Database Administration

• Platform Expertise: Expert-level administration of both InterSystems IRIS and Caché.

Data privacy regulations such as GDPR, LGPD, and HIPAA demand that organizations know exactly where Personally Identifiable Information (PII) lives inside their databases. Yet in practice, most teams rely on manual inventories, tribal knowledge, or external scanning tools that require data to leave the database engine — a process that itself creates privacy and security risks.

This article presents an MVP that takes a different approach: it runs PII detection inside InterSystems IRIS using Embedded Python, analyzing data where it lives and never exporting it to an external process.

#North American Demo Showcase entry. 

>> Answer the question below to be entered in the raffle!

⏯️ ExplantIQ: Ask Your Compliance Data Anything

ExplantIQ is an intelligent data application that tackles one of healthcare's most overlooked financial and regulatory risks: the management of explanted medical device warranty credits. When an implanted device is removed from a patient (due to failure or recall) hospitals are legally required to pursue manufacturer credits, refund payers if the credit exceeds 50% of the device's cost, and report to CMS. Miss that obligation and you're facing a reverse False Claims Act violation. Industry data shows hospitals miss 81% of eligible credits. 

ExplantIQ, built entirely on InterSystems IRIS for Health and DeepSee, solves this by unifying clinical, supply chain, billing, and FDA recall data into a single real-time compliance dashboard, complete with KPI scorecards, trend analytics, and a Text-to-SQL AI Assistant that lets compliance officers query live operational data in plain English. No separate BI tool. No additional architecture. All questions can be answered without leaving your browser tab.

Special thanks to @Emil Polakiewicz and @Boris Mamkin for their contributions. 

🗣 Presenter: @Alyssa Ross, Sales Engineer at InterSystems

Hi Developers,

We are happy to announce the new InterSystems online programming contest:

🏆 InterSystems Programming Contest: AI Agents for FHIR🏆

Duration: May 25 - June 14, 2026

Prize pool: $12,000

InterSystems IRIS globals are one of the platform's core strengths: they store hierarchical data in a direct, ordered, and efficient structure. But when working from Python, manipulating globals can sometimes feel closer to a low-level API than to the natural habits of the language.

The iris-global-reference project provides a Python layer on top of IRIS globals. Its goal is simple: make access to globals more readable, more idiomatic, and easier to integrate into modern Python code, without hiding the underlying hierarchical model.

What was your READY experience like? 🎉
We’ve put together a bingo card — check out how many squares match your experience!

Mark the moments that happened to you or share them in the comments below.
And if you had a memorable READY moment that’s not on the card, tell us about it — we’d love to hear your story ✨

What is wrong with the *inc file code below:

#define ArrayToString(%array,%out,%del)      set %out="" for { ##continue
                                                                                   set key=$order(%array("")) quit:key="" ##continue
                                                                                   set %out=%out_%del_key ##continue
                                                                               } quit

Compilation at calling classmethod as : Set tQStr = $$$ArrayToString(pQArray,tQStr,"##") brings error as:

I need to analyse and improve the performance on some old SQL statements.

One statement uses NVL in the WHERE clause (no wonder why it's slow) but I still have to improve the performance and the data really needs to be the same when returned.

SELECT *
FROM TOURHead, TOURFIND, SGNRFIND, TNRHead
WHERE TOURHead.cl = '123'
AND TOURHead.cl = TOURFIND.cl
AND TOURHead.TOURNR = TOURFIND.TOURNR
AND TOURFIND.cl =* SGNRFIND.cl
AND TOURFIND.SGNR    =* SGNRFIND.SGNR
AND TOURFIND.cl = TNRHead.cl
AND NVL(TOURFIND.TNR, SGNRFIND.TNR) = TNRHead.TNR;

Really hope someone can help me with this one.

I want to develop a REST API for uploading PDF files. I started with OPENAPI spec file. When I test, I get error 8727 referencing missing required parameter file.

"{"$c(13,10,9)"""errors"":[ {"$c(13,10,9,9,9)"""code"":8727,"$c(13,10,9,9,9)"""domain"":""%ObjectErrors"","$c(13,10,9,9,9)"""error"":""ERROR #8727: Parameter required: file."","$c(13,10,9,9,9)"""id"":""RESTRequired"","$c(13,10,9,9,9)"""params"":[""file"""$c(13,10,9,9,9)"]"$c(13,10,9,9)"}"$c(13,10,9)"],"$c(13,10,9)"""summary"":""ERROR #8727: Parameter required: file.

The recently published tutorial "Introduction to InterSystems Data Studio" inspired me to check out this product. And I think it’s an interesting look at how to manage a data fabric without deep-diving into complex code. It allows you to connect disparate data silos, transform the data through automated pipelines, and load it into a unified environment for analysis. So, I decided to write up an example of how you can use it. Basically, I'll walk you through the tutorial in case you don't have time to do it on your own. Though I would definitely suggest you actually follow the tutorial - it has lots of useful information.

To see how it works, I stepped into the shoes of a system administrator, logging in with the provided credentials to explore the interface. The layout centers on a few core pillars: defining where data comes from, cataloging its structure, and building automated "recipes" to move it into production.

And the very first step is to establish a connection to my data. 

Sorry for the weird question, but mumps skills are very rusty.

Am trying to create globals dynamically, but with using names from an array. For example: 

S Names="A,B,Y,Z"
S A=$L(Names,"$")
F I=1:1:A D
//Create globals based on names from database
//^A,  ^B,  ^Y, ^Z etc..  

Many organizations that operate systems built on legacy technology stacks are facing significant support and maintenance complexities. They are eager to modernize, but the transition is usually prohibitively complex and expensive. These challenges apply to virtually any legacy tech, while InterSystems-based systems have their own unique nuances.

Key modernization challenges include:

• Refactoring massive amounts of code, including identifying and removing obsolete "dead code."
• Managing complex business logic accumulated organically over decades.

Hi everyone,

I’m thrilled to share that after several years of deep diving into the InterSystems IRIS data platform, I have finally summarized my project experiences into a new book IRIS (Data Platform) Programming Technical Guide. It is published by Beihang University Press, a prestigious central-level comprehensive publisher renowned for its leading role in aerospace, science, and technology publishing.

Writing this book was a significant engineering challenge for me. My goal was to bridge the gap between "understanding the syntax" and "building a production-ready project.

In our previous article, we explored the basics of unit testing in IRIS and the ways to apply it to a REST API. We even figured out how to test logic before finalizing network configurations and authentication, allowing us to focus solely on testing the API contents. Today, we will build upon that foundation and elevate our unit testing strategy by using another tool: %Populate .

At first glance, the %Populate class appears to be very simple. You can create a class that extends both %Persistent and %Populate and inherit a Populate method designed to generate randomized records.