Hi Developers, I'm currently doing a demo about building a front end UI doing data analytics and setup a performance test with large data objects, therefore using "Populate Utility" could help me auto generate some sample data that I can play with.
Within this post I would like to share my experience of using Populate Utility, including using POPSPEC Parameter.
With my basic knowledge of Docker container and REST API, I would like to have my first try on using InterSystems API manager to take control of APIs and microservices. I have completed this online course using my local IRIS instance as host (Windows OS) and IAM running on a Linux VM (guest).
Firstly, I will introduce InterSystems API Manager (IAM), then explain the steps for setting up my local environment and lastly walk through the course chapters.
Hi Developers,
IntegratedML is a feature helps us and our teams easily implement machine learning (ML) without dedicated ML experts and data scientists. If you do not need particularly complex ML function, integratedML is a good choice and convenient that only requires executing 3 SQL queries to build predictive models directly from InterSystems IRIS to ML engine.
I have recorded a video that walk through the basic of IntegratedML and implement an application from open exchange called diseases predictor by @Yuri Marx
Check this out: Link to my video
Please feel free to leave any comments
ZPM is designed to work with applications and modules for InterSystems IRIS Data Platform. It consists of two components, the ZPN Client which is a CLI to manage modules, and The Registry which is a database of modules and meta-information. We can use ZPM to search, install, upgrade, remove and publish modules. With ZPM you can install ObjectScript classes, Frontend applications, Interoperability productions, IRIS BI solutions, IRIS Datasets or any files such as Embedded Python wheels.
Today this cookbook will go through 3 sections:
Hi Developers,
Python has a large and powerful ecosystem that contains thousands of libraries and packages available, especially in data science.
Therefore, I wanted to have a first try in using a recent feature of IRIS called Embedded Python, to simply import a python library called datetime, generate data with a timestamp component and persist it in InterSystems IRIS for Health Data Platform. The same will work on IRIS Data Platform as well.
I am happy to share with you my first experience of using a docker container version of IRIS for Health to explore your interest in using or having a trial by taking the advantage of a docker container that is lightweight, and easy to deploy. This cookbook will go through the implementation steps using the GitHub repository called ENSDEMO written by Renan Lourenco.
Steps for setting up an IRIS for Health Community Edition docker container environment
docker pull containers.intersystems.com/intersystems/irishealth-community:2022.2.0.304.0